Estimating forest cover change and forest carbon stock by using remote sensing and GIS in phnom tamao zoological park and wildlife rescue center cambodia

This study presents the results of forest cover change in 5 selected from 1997 to 2017 and carbon stock assessment of Phnom Tamao Zoological Park and Wildlife Rescue Center PTWRC.. This

MINISTRY OF EDUCATION AND TRAINING MINISTRY OF AGRICULTURE AND RURAL

CAMBODIA

MASTER THESIS IN FOREST SCIENCE

MINISTRY OF EDUCATION AND TRAINING MINISTRY OF AGRICULTURE AND RURAL

CAMBODIA

Major: Forest Science

Code: 8620201

MASTER THESIS IN FOREST SCIENCE

Supervisor: Dr Manh Hung Bui Signature:………

Hanoi, 2018

Table of Contents

Table of Contents i

List of Table vi

Abstract vii

CHAPTER I: INTRODUCTION 1

1.1 Background 1

1.1.1 Decline of forest resource 1

1.1.2 Deciduous forest in Cambodia 3

1.1.3 Remote Sensing, satellite imagery and GIS 3

1.1.4 Carbon stock and sequestrations 4

1.2 Objective 5

1.2.1 General objective 5

1.2.2 Specific Objective 5

CHAPTER II: LITERATURE REVIEW 6

2.1 Overview about remote sensing and GIS 6

2.1.1 Remote sensing and GIS in forestry sector and image classification 6

2.1.2 Land use and Land cover change studies 7

2.2 General information about carbon stock 10

2.2.1 Methods for assessment of above-ground biomass and carbon estimations 11

2.2.2 Ground-based forest inventory method 11

2.3 Theories and definition 12

2.3.1 Land use and land cover (LULC) change 12

2.3.2 Land Cover 12

2.3.3 Land use 12

3.2.1 Tools 17

The study materials used in this study, are listed in Table 1 17

3.2.2 Software 17

3.3 Data collection 18

3.3.1 Image Acquisition and Pre-processing 18

3.3.2 Data Collection (Fieldwork) 19

3.3.3 Assessment of Forest Carbon stock 20

3.3.3.1 Plot establishment method 20

3.3.3.2 Tree data collection 21

3.4 Data Analysis 24

3.4.1 Tool and Computer software was used for data analysis 24

3.4.2 Image processing 25

3.4.3 Allometric equation and Carbon stock estimation 29

3.4.4 Stand information 32

3.4.5 Descriptive statistics for height and diameter variables 33

3.4.6 Pearson correlation 34

CHAPTER IV: RESULTS AND DISCUSSION 38

4.1 Land use Land cover (LULC) classes of Phnom Tamao Zoological 38

4.1.1 Forest status assessment in PTWRC 38

4.1.1.1 Forest cover analysis of PTWRC in 2017 38

4.1.1.2 Forest cover analysis of PTWRC in 2013 39

4.1.1.3 Forest cover analysis of PTWRC in 2007 40

4.1.1.4 Forest cover analysis of PTWRC 2001 41

4.1.1.5 Forest cover analysis of PTWRC 1997 42

4.1.2 Spatial forest cover change evaluation 44

4.1.2.1 Forest cover conversion of PTWRC from 1997 to 2001 44

4.1.2.2 Forest cover conversion of PTWRC from 2001 to 2007 45

4.1.2.3 Forest cover conversion of PTWRC from 2007 to 2013 47

4.1.2.4 Forest cover conversion of PTWRC from 2013 to 2017 48

4.1.2.5 Forest cover conversion of PTWRC from 1997 to 2017 49

4.1.3 Land Use Land Cover (LULC) proportions of PTWRC 2017 50

4.1.4 Accuracy assessment of the LULC map for 2017 of PTWRC 51

4.2 Above-ground Biomass and Carbon stock 52

4.2.1 Dominant tree species 52

4.2.2 Tree density 54

4.2.3 Distribution of diameter classes 57

4.2.4 Aboveground tree biomass and Carbon stock 58

4.2.5 Correlation and Carbon stock map 60

4.3 Discussion on spatial pattern of forest cover change and carbon stock 63

4.4 Propose solution and recommendation for sustainable forest management 64

CHAPTER V: CONCLUSION 66

ACKNOWLEDGEMENTS 68

REFERENCE 69

APPENDIX 75

LIST of figure

Figure 1: Study area map 14

Figure 2: Photograph showing the LULC of PTWRC 16

Figure 3: Tool for assess this research study 17

Figure 4: Satellite Image of the study area 19

Figure 5: Landsat ETM 2007-satellite images and GPS points in the study area 19

Figure 6: Plot establishment 20

Figure 7: Determining a side direction; Figure 8: Measurement distance 21

Figure 9: Measuring plot of DBH 22

Figure 10: Measuring height by using Blume-Leiss ; Figure 11: Measuring DBH by using Caliper……… 22

Figure 12: Measured vairable of tree 23

Figure 13: Scatter inventory plot location 24

Figure 14: Flowchart of methods, Green boxes are main steps to analyze 25

Figure 15: Flowchart of methods to calculate carbon stock and carbon stock map 37

Figure 16: NDVI classified map in PTWRC in 2017 39

Figure 17: Area (ha) land cover classes 2017 39

Figure 18: NDVI classified map in PTWRC in 2013 40

Figure 19: Area (ha) of land cover classes 2013 40

Figure 20: NDVI classified map of 2007 41

Figure 21: Area (ha) of Land cover classes in 2007 41

Figure 22: NDVI classified map in 2001 42

Figure 23: Area (ha) of land cover in 2001 42

Figure 24: NDVI classified map 1997 43

Figure 25: Area (ha) of land cover 1997 43

Figure 26: Forest cover change map of PTWRC from 1997-2001 45

Figure 27: Chart of area conversions of PTWRC in (ha) and (%) from 1997-2001 45

Figure 28: Forest cover change map of PTWRC from 2001-2007 46

Figure 29: Chart of conversion area (ha) and (%) of PTWRC from 2001-2007 46

Figure 30: Forest cover change map of PTWRC from 2007-2013 47

Figure 31: Chart of conversion area in (ha) and (%) of PTWRC from 2007-2013 47

Figure 32: Forest cover change map of PTWRC from 2013-2017 48

Figure 33: Chart of conversion area in (ha) and (%) of PTWRC from 2013-2017 48

Figure 34: Forest cover change map of PTWRC from 1997-2017 49

Figure 35: Chart of conversion area in (ha) and (%) of PTWRC from 1997-2017 49

Figure 36: The grapical shows of the PTWRC proportion for 2017 50

Figure 37: LULC map of PTWRC in 2017 51

Figure 38: Diameter class (cm) 57

Figure 39: Height class distribution of PTWRC 58

Figure 40: Relationship between DBH and Height with the aboveground biomass 60

Figure 41: Relationship between DBH and Height with Carbon proportion 61

Figure 42: Ralationship between ABG and carbon stock 61

Figure 43: Carbon stock map of PTWRC in 2017 62

List of Table

Table 1: Research materials used 17

Table 2: Landsat image 18

Table 3: Raster calculation for change detection 28

Table 4: Wood-specific density calue of different tree species 30

Table 5: Descriptive analysis of the LULC 38

Table 6: Land cover from 1997-2017 (ha) 38

Table 7: Forest cover change detection in selected years periods in (ha) 44

Table 8: Forest cover change in percentage 44

Table 9: The spatial extent of LULC after classified of PTWRC in 2017 50

Table 10: Accuracy assessment for LULC map 2017 51

Table 11: Accuracy total 52

Table 12: List of trees species in PTWRC 53

Table 13: Number of trees in plots at the PTWRC and their DBH rang 55

Table 14: Summary of decriptive statistics for DBH and H 56

Table 15: Frequency and percentage of DBH and Height 58

Table 16: Summary value of aboveground tree biomass, tree density and vilume of tree 59 Table 17: Descriptive statistics of ABG, Carbon, volume, and basal area 60

Table 18: Comparision of carbon stocks in different conutries and forest type 62

ABSTRACT

All aboveground about 80 % deposited by forest and 40% of all belowground native as organic carbon, building forest ecosystems essential to conserving the global carbon balance and mitigating climate change [1] The amount of forest carbon stored is differed according to chronological factors such as species, forest type, size, age, stand structure, ecological zone, and another thing Forest covers from satellite data provide the various scales from the past periods, in particular, it’s have been conducted all over the world included Cambodia for several years Remote Sensing is well recognized as an important source of information to quantify forest extents in large areas in previous and present time [2]

This study presents the results of forest cover change in 5 selected from 1997 to

2017 and carbon stock assessment of Phnom Tamao Zoological Park and Wildlife Rescue Center (PTWRC) This will be helpful in the future for forest cover and carbon stock (sequestration rate monitoring after established until the present to conserve biodiversity and sustainable forest management for involving mitigate climate change in the study area (PTWRC)

Using multi-temporal remote sensing data to quantify forest cover and land use land cover change was conducted in PTZPWRC, Ba Ti district, Takeo commune, Cambodia during 1997 – 2017 For this study, Landsat data including Landsat 5 (TM) in

1997, 2001, 2007 and Landsat 8 (OLI) in 2013, 2017 with a spatial resolution of 30 m was used to quantify forest cover extents and defined the driver of change NDVI (Normalized Difference Vegetation Index) in combination with unsupervised classification was used After analyzed the results showed that there was a change from years 1997 - 2017 of forest cover change extents Accuracy assessments of forest cover maps showed that highly

structure of the trees, which is diameter at breast height (DBH), height (H) and wood density consider as a major parameter The biomass equation of the tree had been developed by Chave et al (2014) was taken to estimate ABG and carbon stock for this study area which considers based on tropical forest Total biomass was converted to carbon

by using conversion factor 0.47 suggested by the Intergovernmental Panel on Climate Change [3] In this study, data had been collected in a deciduous forest in Phnom Tamao Zoological Park and Wildlife Recuse Center, Cambodia

A total of 30 Forest inventory sample plots by using simple random sampling design scatter in the forest of PTWRC Carbon was collected from one carbon pools; aboveground biomass A sample size of 500 m2 plot with 25 lengths and 20 widths had been used during data collection Forest inventory was measured of tree DBH, height and species identification per plots The dominant species of this studies are from families of

Dipterocarpaceae, Burseraceae, Chrysobalanaceae, and Euphorbiaceae The number of

tree sample is about 2,386 trees, height range between 1 until 15 m and diameter range between 5 to 27 cm The average of trees per plot about 79 trees approximately about 1,580 per hectare The data analysis was calculated using Microsoft Excel and SPSS software, after which it was offered in a tabular form as well as in diagrams and figure The total mean of aboveground biomass approximately about 62.238 t ha-1 and an average

of carbon stock estimated about 29.25 t ha-1

In the future, regular monitoring of forest cover and forest carbon stock is recommended to assess of fluxes forest cover and carbon stock and other ecosystem services generated by Phnom Tamao Zoological Park and Wildlife Rescue Center (PTWRC) The results and information generated by this study will also be contributed for PTWRC in order to investigate forest management and biodiversity conservation in the study area

CHAPTER I: INTRODUCTION

1.1 Background

1.1.1 Decline of forest resource

Cambodia is a country located in South-East Asia with a total area of 181.035 km2[3] In 1965 Cambodia forest cover was estimated at approximately about 13,227,100 ha, about 73.04% compared to the total area [4] Nevertheless, Cambodia forest cover was declined rapidly from 1965 to 2006 to 10,730,781 ha, about 59.09% [5] There are three main forest types in Cambodia such as semi-evergreen forest area decreased 1.6% per year (approximately about 23,114.3 ha year-1); deciduous forest 0.7% (about 35,258.7 ha) and evergreen forest 0.3% per year (12,903.6 ha) between 2002 and 2006 [6]

Before 1995, The forest area was destroyed by human activities such as deforestation, agriculture expansion, hunting, land grabbing, land encroachment for agriculture village expanding and construction influence on shifting patterns of land use are a primary component of many current environment concerns as land use land cover (LULC) change is gaining recognition as a key driver of environmental change [7] In additionally, Deforestation in Cambodia is a political issue In recent years forest resources have been devastated by war, corruption, political rivalry, and military control, encouraged

by the demand of neighboring countries Timber revenue funded both sides in the prolonged civil war between the Khmer Rouge and the Phnom Penh government Cambodian political, military and business elites have privately benefited from the sale of these state resources [8]

Increasing rapidly of the population in the country and its related increase is a major factor which has altered natural vegetation cover This has resulted in a suggestive

cover in Cambodia relation to the population growth from 1958, the rapid increase of population has continuously led to the increase of demands for fuelwood and agriculture lands

The staple diet of Cambodia is rice About 92% of Cambodians use either firewood

or charcoal for their daily energy needs and about 85% of the population are involved in agricultural activities With the population growth rate increasing faster than ever 1.6-3% and rice production in recent years having stabilized or decreased, the population had to increase the area of land cleared to provide more rice and other sources of food [11] In North east Cambodia where most hill-tripes occur shifting cultivation accounted for 2.5%

of the land area This means that 300,000 ha of forest was cleared for agriculture between

1958 and 1964 of which about 10,000 ha of dry Dipterocarp forest was converted into savanna each year due to uncontrolled fire political instability meant that further information was unobtainable [6]

The rapid increase of logging companies in Cambodia clearly shows that forests are needed as raw materials to supply their factories Amounts of 180,000 m3 and 385,000 m3were found to have been cut illegally in 1996 and early 1997, respectively Furthermore, about 92% of Cambodians firewood and charcoal for cooking energy [12]

Lack of trained human resources, there are numerous obstacles to develop and implement rational resource management practices owing to the lack of trained human resources, funding, socioeconomic development and control of forest areas There are only two forestry institutions, there are Royal University of Agriculture (RUA) and PRLAS, only about 17-25 students and trainees graduated annually [13]

Forests stock is about 80% of all aboveground and 40% of all belowground earthly organic carbon, building forest ecosystems crucial to maintaining the global carbon balance and mitigating climate change [14] The initial carbon stocks for three main forest type including aboveground, below ground, dead wood, litters by pools in 2002 such as: for evergreen forest, total carbon stocks (CS) 164.8 MgC ha-1 and 604.3 MgCO2 ha-1, Semi-evergreen forest total CS 154.9 MgC ha-1 and 567.6 MgCO2 ha-1, deciduous forest

CS 150 MgC ha-1 and 550.2 MgCO2 ha-1 [6]

1.1.2 Deciduous forest in Cambodia

Forest management in Cambodia has been a challenging task for the Cambodian government especially Forest Administration (FA) from the 1950s to the present [15] The Forest Administration (FA) plays an important role as government activity in the forestry sector such as conservation, restoration, and enrichment in Cambodia In addition, Forest Administration was established in 2003 (replacing the former Department of Forestry & Wildlife) [8]

In Cambodia, there are 20 provinces have been covered by forest Seven major forest types are recognized in Cambodia, they are evergreen, semi-evergreen, deciduous, Flooded, Bamboo and mangrove forests Cambodia has total deciduous forest cover of 4,613,417 ha that corresponding about 25.40% of total area in 2006 [5] Deciduous forest plays a significant role for providing good and services to human and wildlife, such as firewood, food (mushroom, honey, bamboo shoot, leaf, fish.), carbon sequestration, pollution absorption, wood for construction, landscape amenity, recreation, water supply, climate regulator, habitats for wildlife and biodiversity [16] The deciduous forest in Cambodia has reduced dramatically to 3,480,532 ha, approximately 19.17% of Cambodia’s land cover from 2006 to 2014 [17] The driver of deforestation and forest degradation due to unsustainable management and human activities Moreover, human activities that cause loss of forest: weak forest law enforcement, overexploitation, forest fire, land encroachment, agriculture expansion and illegal logging Therefore, protecting and reestablishing the forest have become the main task for Cambodian Forest Administration, local authorities and organizations [18]

1.1.3 Remote Sensing, satellite imagery and GIS

The study area lack of data and document related to land use land cover and forest

reflections of soil and rocks are often much more than the reflection of sparse vegetation that leads to the separation of plant signals difficult [20] Satellite image classification, change analysis and econometric modelling are extensively used to identify the rates and drivers of deforestation in global hotspots of biodiversity and tropical ecosystems [21] Recently, a joint collaborative effort between NASA (National Aeronautics and Space Administration) and World Conservation Union, IUCN (International Union for Conservation of Nature) for conserving the biological diversity in terms of driver of changes in forest cover and rate of deforestation in overall 34 global hotspots has been signed However, many airborne and satellite sensors with high spatial and spectral resolution, are currently available, to study land cover changes for over the last decades such as Landsat (approximately 30 m pixel size) Landsat is a series of US satellites launched between 1972 and 1999 (Arvidson et al., 2006) for monitoring the temporal and spatial changes in land cover [22]

Satellites data have become a major application in change detection because of the repetitive coverage of the satellites at short time intervals [23] Using remote sensing, spatially explicit time series of environmental data can be quickly obtained and update (Dewan and Yamaguchi, 2009), with GIS (Geographical Information System) techniques provide information about landscape history, topography, soil, rainfall, temperature and factors on which the distribution of species depends [24, 25]

In additionally, Remote sensing in used to provide opportunities to assess carbon and the ultimate benefit of using remote sensing data to estimate carbon stock in forest biomass The importance of remote sensing for the quantifying, monitoring, and mapping

of carbon stock by interpolation method

1.1.4 Carbon stock and sequestrations

Forests play an important role in balancing the Earth’s carbon dioxide (CO2) supply and exchange [26] Quantifying carbon in a forest biomass, especially in a tropical forest,

is not a straightforward task because of the complex structure of the forest stand and the interaction between vegetation, soil, and atmosphere The Carbon sequestration is a ration that can be taken up to mitigate climate change In addition, the capacity of Carbon accumulated in forests differs according to spatial and temporal factors such as forest type,

size, age, stand structure, associated vegetation, and ecological zonation, among another thing [27] The forests store the most carbon with the popular of sequestered carbon thought in wooden biomass A forest stand parameter, such as diameter at breast height (DBH) is easily measured and height in the field and readily available from forest inventories Once an allometric equation has been established, this equation can be used to estimate tree biomass

In this study, the allometric equations of Chave 2014 was used to achieve a better estimation of above-ground biomass for tropical forest [28] Moreover, in the study area no one conduction any research about above-ground biomass and carbon stock Amount of carbon stock provide a data base to observant on forest health according to size and carbon storage in the tree

This study of the forest in the Phnom Tamao Zoological Park and Wildlife Rescues Centre (PTZPWRC) was conducted to better understand forest carbon stocks and forest cover change during years selection of the forest situation by using Remote Sensing and Geographic Information System (GIS) The study also created opportunities to associated information to whom may concern

1.2 Objective

1.2.1 General objective

This study aimed at analyzing the forest cover changes and forest carbon stock to manage the forest resources sustainably in the Phnom Tamao Zoological Park and Wildlife Rescues Centre, Cambodia by using multi-temporal Remote Sensing (RS) data and GIS-based techniques

1.2.2 Specific Objective

CHAPTER II: LITERATURE REVIEW

2.1 Overview about remote sensing and GIS

Remote Sensing, Global Positioning System, and Geographical Information System technologies have becoming very useful tools in number of applications by providing gigantic selection in terms of spectral, temporal and spatial resolution Using these three systems, can develop an information system effectively for forest resource and management to use in the comparison and evaluation of various species to realize the scientific management of forest resources Information systems for forest management have becoming a very important tool for quick and easy decision making As the activities

of forestry sector are expanding, the need to develop a suitable management information system is being essential task

In this present research work developed a forest cover detection for Phnom Tamao Zoological Park and Wildlife Rescue Center, Cambodia using high spatial resolution of satellite imageries for the identification of forest cover supported with extensive field survey using GPS

2.1.1 Remote sensing and GIS in forestry sector and image classification

Giriraj et al from forest survey of India, built up a very precise Remote Sensing based forest cover assessment maps for forest resource management The vastness of forest resources and high forest type of diversity facing challenging gaps in forestry database management However, with the advent of geospatial technologies like Remote Sensing (RS), Global Positioning Systems (GPS), and Geographical Information System (GIS) proved very useful for forest management, with importance on developments made during the last decade Forest resources information needs to be set run and also update at both divisional and national level in India At the divisional level, it requires periodic updates with all the related information of that division and at the national level an evaluation of tree and forest cover is needed for ascertaining the economic, ecological, and even social value of the green cover carries In 1980s, after NRSA displayed the potential of forest evaluation and forest cover mapping, Forest Survey of India built capacity for highly accurate Remote Sensing based forest cover assessment with perfection in scale of interpretation and resolution of the imagery

Such spatial database created/produced on maps is of huge incentive to the organizers, leaders and arrangement creators at the national, state, local level GPS in blend with RS and GIS likewise has been utilized by FSI for execution of reasonable inspecting plan for forest in remote zones [29]

According to Peter Leimgruber et al, Myanmar is one of the most forested countries

in mainland South-east Asia These forests support a large number of important species and endemics and have great value for global efforts in biodiversity conservation Landsat satellite imagery from the 1990s and 2000s was used to develop a countrywide forest map and estimate deforestation The country has retained much of its forest cover, but forests have declined by 0.3% annually Deforestation varied considerably among administrative units, with central and more populated states and divisions showing the highest losses Ten deforestation hotspots had annual deforestation rates well above the countrywide average Major reasons for forest losses in these hotspots stemmed from increased agricultural conversion, fuelwood consumption, charcoal production, commercial logging and plantation development While Myanmar continues to be a stronghold for closed canopy forests, several areas have been experiencing serious deforestation [30]

2.1.2 Land use and Land cover change studies

According to Xiao, J (2006), China, the earth surface is very unique in its cover in

every parcel of land The earth’s surface has characteristics of Land use and land cover which are distinct yet closely linked Agriculture, Grazing, Logging, Mining and Urban development among many others are the uses of land which could be predicted The categories of land cover are roads, wetland, forest, cropland, urban areas and pasture etc Land cover, the term initially referred to the type and state of vegetation, such as forest or grass cover but it has widened in successive practice to include other things such as human

and inter-linkage of human activities and environmental status Lenche Dima and Kuhar Michael of Amhara region, Ethiopia, has taken the study to understand this variability by using Landsat MSS, TM, ETM+ and ASTER satellite images of 1972/3 to 2005 duration The methodology followed include assessment, inspection of the study area, gathering field data and derived data and processing it using software’s like ERDAS 9.1, ENVI 4.3, ArcGIS 9.2 and MS office [32]

According to Zubair and Ayodeji Opeyemi, 2006, Quantitative evaluation of the alterations occurred between 1972 and 2001 were carried out through calculating Land Consumption Rate and Land Absorption Coefficient of the LU-LC in Ilorin with the aid of

RS and GIS which revealed ascend of built up land between 1972 and 1986 and decrease

of the same during the period 1986-2001, the same latter variations trend is forecasted by

2015 [33]

According to Olorunfemi in 1983, that the evaluation by using remote sensing to estimate the index of change done by the superimposition of land use land cover images and land use maps of 1972, 1984 and1990, done to study the pattern of change in the area, otherwise difficult with the traditional method of surveying Olorunfemi observed this when he was working on a case study with Ilorin in Nigeria by aerial photographic approach to monitor urban land use in developing countries [34]

According to Mutie S.M, et al 2006, the seasonal Land Sat MSS, TM and ETM images of 1973, 1986 and 2000 respectively were analyzed for Land cover change Between 1973 and 2000, forests and shrub-land have reduced by 32% and 34% respectively was depicted from the Digital image analysis using IDRISI Kilimanjaro A reduction by 45%, 26%, and 47% respectively was also seen in Grassland, savannah and water bodies However, an overall increased by 100% has been observed in agricultural land, tea and open forests, and wetlands The semi distributed United States Geological Survey Geo Spatial Stream Flow Model was used to investigate the effects of the derived land cover changes on river flow in Mara River The year 2000, land cover data produces higher flood peaks and faster travel times weigh against to the 1973 land cover data is revealed through the Simulation results The effects of land use pressure in the basin are indicated by the changes detected [35]

An image analysis study in the Kucukcekmece Watershed (Metropolitan Istanbul, Turkey) using multi temporal digital satellite imagery and land use/land cover changes from 1992 to 2006 is presented by H Gonca Coskun, et al in 2008 Portions of Kucukcekmece District are included in the Kucukcekmece Basin, within the Istanbul municipality leading to a dramatic urbanization First a land cover classification was implemented with an urban monitoring analysis approach followed by determining changes in land cover by a change detection method controlled with ground truth information The inconsistency and enormity of hydrological components were cumulatively influenced by urban sprawl which was based on land-use patterns in the watershed during the study period Land-use monitoring, planning, and management of urbanized watersheds is enhanced by the use of Remote Sensing and GIS combined techniques, as an effective tool, is the proposed approach in the study [36]

Isin Onur and et al 2009, are investigated the urban sprawl effect on the small village (Kemer) in the Mediterranean region of Turkey, turning it into an internationally popular touristic destination Approximately 30 years of land cover and land use changes were analyzed using Land sat Multispectral Scanner data of 1975 and Thematic Mapper data of 1987, 1995 and 2003 by image classification techniques The Coordination of Information on the Environment (CORINE) methodology was used as a base in the land use hierarchy GIS was used in Data organization and collection stages Finally, the results showed that, from 1975 to 2003, most of these areas were structured and permanent crops decreased by 75% and 55% decrease observed in heterogeneous agricultural areas and there had been no markable change in forests [37]

Yitaferu in 2007 has made satellite image study of the Lake Tana basin between 1985/86 and 2001/03 He observed that croplands raised by about 4.2%, which mostly

imagery and GIS for the analysis of the geographic diffusion of riverine ecological decline

in the Niger River basin In this study, ArcGIS was used to demarcate the drainage basin, and relate Global Forest Watch Cameroon auxiliary data such as hydrology and settlements from the Upper Noun, after radiometric and atmospheric correction, and ENVI to multi-

spectral classification of the Landsat images [39]

NDVI image classification According to Aamita Manandhar.,

2009.The Normalized Difference Vegetation Index (NDVI) is one of the mostwidely used numerical indicator that uses the visible (VIS) and near-infrared bands (NIR) of the electromagnetic spectrum, and is utilized to analyze remote sensing images and assess whether the target contains live green vegetation or not NDVI was first applied

to classify Landsat-MSS of 1985 and Landsat-TM of 1995 and 2005 The major LULC identified were Woodland, Pasture/scrubland, Vineyard, Built-up and Water-body By applying post-classification correction (PCC) using ancillary data and knowledge-based logic rules the overall classification accuracy was improved from about 72% to 91% for

1985 map, 76% to 90% for 1995 map and 79% to 87% for 2005 map The improved overall Kappa statistics due to PCC were 0.88 for the 1985 map, 0.86 for 1995 and 0.83 for

2005 [40]

Accuracy assessment of classified image, Congalton, R G., 1991 The categorization processes’ degree of acceptability is estimated for accuracy assessment in the change detection process of image classification the use of the error matrix and the standard method for one-point-in-time land cover products in standard accuracy assessment procedure for baseline land cover products is very complex to relate to multi-temporal change analysis products The techniques are well recognized for small areas and single time periods evaluating the precision for large areas, past time periods, and change databases can become challenging as it will be not easy to obtain a sufficient database of historical reference materials So, accuracy measurements are generally restricted to recent image that serves as a reference using GCP’s collected as part of the data required for the variation study [41]

2.2 General information about carbon stock

Perennial vegetation is the most important element in the terrestrial carbon

sequestra- tion Their key role in ecosystem dynamics is well known However, it is paradoxical that the vegetation has undergone destruction and degradation in the modern times due to industrial and technological advancement achieved by the human society This advancement has resulted in emission of carbon dioxide Therefore, there is an imper- ative need to address environmental issues related to them Trees are important sink for atmospheric carbon (i.e) carbon dioxide, since 50% of their standing biomass is carbon itself Importance of forested area in carbon sequestration is already accepted and well documented Ever green fruit trees, and needle leaf vegetation also have similar carbon sequestration ability as that of forest trees But, hardly no attempt has been made to study and classify the area based on the quantity of CO2 sequestred In this study, we made an attempt to predict the carbon sequestration in cashew and casuarina plantation of Tamil Nadu using remote sensing and ecosystem modeling The literature pertaining to the study

is reviewed hereunder

2.2.1 Methods for assessment of above-ground biomass and carbon estimations

According to FAO 2000., Biomass is a crucial element in the carbon cycle, has defined biomass as “the organic material both above and below the ground, and both living and dead, e.g., trees, crops, grasses, tree litter, roots, etc.” Biomass consists of Above-Ground Biomass (AGB) and Below-Ground Biomass (BGB) where AGB includes all living biomass above the soil, while BGB is the biomass of live roots more than 2-millimeter diameter Majority of biomass assessments are done for AGB of trees because it typically forms the largest pool of total living biomass in the forest and it is the most directly impacted by degradation and deforestation There are different approaches in practice to measure AGB and consequently the carbon stock of forests Lu, (2006) reviewed and summarized different approaches to estimate biomass based on field

can be further converted for estimation of carbon stock by applying allometric relationships GIS-based methods are then used based on extrapolation of existing forest inventory volume data and ancillary data such as land cover type, site quality and forest age to establish an indirect relationship for biomass in an area [43]

2.3 Theories and definition

2.3.1 Land use and land cover (LULC) change

Land-use and Land-cover change is a general term from the human modification of Earth’s terrestrial surface Though humans have been modifying land to obtain food and other essentials for thousands of years, current rates, extents and intensities of LULC are far greater than ever in history, driving unprecedented changes in ecosystems and environmental processes at local, regional and global scale[44]

2.3.2 Land Cover

Land cover refers to the physical and biological cover over the surface of the land, including water, vegetation, bare soil, and/ or artificial structure Land-cover denotes the surface cover over land, including vegetation, rock and human-modified surfaces such as buildings[44]

2.3.3 Land use

Land use is a more complicated term Natural scientists define land use in terms of syndromes of human activities such as agriculture, forestry and building construction that alter land surface processes including biogeochemistry, hydrology, and biodiversity Social scientists and land managers define land use more broadly to include the social and economic purposes and contexts for and within which lands are managed (or left unmanaged), such as subsistence versus commercial agriculture, rented vs owned, or private vs public land [44]

2.3.4 Natural Forest

Natural Forest is forests that meet the natural ecosystem definition irrespective of their use They may be in conservation or logged for wood production: the key point is that their ecological structure and functions have not been degraded such that natural regenerative processes can no longer operate to recover the canopy structure following

disturbance [45] Land with a tree canopy cover of more than 10 % and area of more than 0.5 ha the forest is determined both by the presence of trees and the absence of other predominant land uses The trees should be able to reach a minimum height of 5 m [42]

2.3.6 Aboveground biomass

Forest biomass is a necessary factor in environmental and climate forming Also, standing forest biomass is an important active contributor in the global carbon cycle [46] The carbon stock is the term used for the C stored in terrestrial ecosystems, as living or dead plant biomass (aboveground and belowground) and in the soil, along with usually negligible quantities as animal biomass [47] Aboveground biomass includes all woody stems, branches, and leaves of living trees, creepers, climbers and epiphytes as well as understory plants and herbaceous growth[48]

CHAPTER III: METHOD AND MATERIALS

3.1 Study Area

Data for this investigation were gathered from tests directed at Phnom Tamoa

Zoological Park and Wildlife Recuse Center (PTWRC), Cambodia The study area situated Tro Pang Sap village, Tro Pang Sap Commune (approximately 11° 18′ 00.85″ N

and 104° 48′ 04.85″ E), Ba Ti district, Takeo Province that lies in Cambodia The northern border of the study area with Krang Thnong commune, Ba Ti district, Takeo province The southern border adjoins Kandoeng commune, Ba Ti district, Takeo province And the western adjoins Lumpong commune, Ba Ti district, Takeo province The center was established in 1995 and with an area of over 2,385 hectares of protected regenerating forest, this is the largest zoo in Cambodia [7]

Figure 1: Study area map

The study area located in flat area; elevation is from 80 to 100 m above sea level Elevation tends to increases from North to South and East to West The highest peak in the area is in the middle of PTWRC Average slop is in the range of 10 to 18 degrees

Rainfall: The site encounters a stormy season from June to December Precipitation amid this period represents about 1,200 to 1,500 mm the yearly aggregate Because of the regularity of precipitation, plant development is likewise exceedingly occasional Normal air temperature in the examination zone additionally crests amid the stormy season The mean annual temperature between 22-35 Co

Humidity: The investigation region has moderately high humidity which is generally equally disseminated between months of the year Normal air moisture is 85.0% The most noteworthy normal humidity is in December and January about 89.0%

Soil: In the study area includes two noteworthy soil composes They are the soil varies sandy soil and silty clayey loam that is medium in forest humus The average soil depth is 40-70 cm Average soil moisture is from 20 to 40 % and soil pH range from 5 to 5.9 [7]

3.1.1 Vegetation

The natural forests (deciduous forest) at the study area were previously dominated

by Shorea Siamensis, Shorea obtusa, Canarium album, Aprousa filicifolia, Dipterocarpus obtusifolius, Parinari anamensis, Catunaregam tomentosa Roureopsis stenapetala and

other species, which were destroyed use for fodder, fuelwood, timber for building material, charcoal-making and common forest fires specially in dry season The remaining forest covers almost all naturally regenerated tree species [7, 49]

(a): build-up area (b): forest area

(c): water (d): grassland

(e): forest area (f): road

Figure 2: Photographs showing the LULC of PTWRC

3.2.Materials

3.2.1 Tools

The study materials used in this study, are listed in Table 1

Table 1: Research materials usedLandsat V TM satellite image of 8th July 1997

Landsat V TM satellite image of 01st June 2001

Landsat V TM satellite image of 25th January 2007

Landsat VIII ETM+ satellite image of 27th December 2013

Landsat VIII ETM+ satellite image of 06th December 2017

Caliper for measure tree DBH

Meter tape 50 meters to measure plot size

Global Positioning System (GPS)

Blume liess for measure tree height

Source:[50]

3.2.2 Software

Figure 3: Tool for assess this research study

3.3 Data collection

3.3.1 Image Acquisition and Pre-processing

Landsat TM and ETM+ satellite image of this research study taken the show in Table 2 below:

Table 2: Used Landsat image

(e) RGB image 1997

3.3.2 Data Collection (Fieldwork)

Landsat ETM+ 2017 satellite image and Global Positioning System (GPS) was used to the location of the ground control points in the field The geographic coordinates of the observation sites from the GPS reading were documented and the locations were

indicated on the ETM image (Figure 3)

During the data collection, the accuracy and the distribution of GCPs were taken into account a land use land cover type were randomly selected from the field This was done through the study area by moving around the item

Figure 4: Satellite Image of the study area

3.3.3 Assessment of Forest Carbon stock

Carbon stock accounting is important to determine the carbon stock in the research study The above-ground biomass comprises all woody stems, branches, and leaves of living trees Forest inventories were conducted to measure such as diameter breast high (DHB), height (H), Wood density for calculated above-ground biomass for the study area [51]

The analyses were performed using Microsoft Excel, SPSS 23

3.3.3.1 Plot establishment method

Data were collected from 30 plots of deciduous forest The forest is shown in the following figures

Figure 6: Plot establishment

Simple random sampling was applied to select plot locations The Simple Random Sampling (SRS) suggestion by Zerihun 2013., et al was used in the sampling design because of its simplicity for short-term monitoring and it’s suitable for one type of forest especially deciduous forest [52] The rectangular plot was used 500 m2 within 20 m width and 25 m length were used to measure trees with diameter greater than or equal to 5 cm

To set up the 500 m2 sample plots for tree measurement are as follow the steps below [53]:

- In the sampling areas, with a stake, a start point was set

- One person stood at the starting point and made the direction for the first plot side

- Another person used a measuring tape to measure the distance from the starting point following the direction of the plot side The distance length must be horizontal and at

every 10 m, one stake must be set up All stakes of one side must lie on a straight line The distance of the two sides were 25 x 30 m

- To make sure the plot was a rectangle, all corners formed by two sides must be 90 degrees by applying the Pythagorean Theorem for a right triangle (3 m x 4 m x 5 m) [54]

- After setting up the plot with stake makers at every 10 m on each side of the plot, poly rope was used to border the plot through stake makers

- General information (location, coordinates at plot center) were recorded in the field notes Figure 7 & 8 show some plot establishment activities in the study site

3.3.3.2 Tree data collection

When plot was set up, the diameter at breast height (DBH), total height of all the tree with bole larger than 5 cm were measured and recorded in table 3 [54] Tree diameter was measured by using a caliper [55].The fixed arm was placed along one side of the tree

at 1.3 m height The moveable arm was then placed flush against the other side of the tree and scale was read directly [56].The plane caliper had to be perpendicular to the bole [55]

There are some notes about measured position of DBH as following in Figure 9:

Figure 9: Measuring plot of DBH (Manh Hung Bui, 2016)

Total tree height was measured by using Blume Leiss, a height measuring instrument of medium size and weight [56] Figure 10 & 11 show diameter and height measurement

Figure 10: Measuring height by using Blume-Leiss Figure 11: Measuring DBH by using Caliper

To measure the total height, the following steps were implemented [56]:

- Selected a position, 15 m horizontal distance from the base of a tree where the tree tip and base can be seen

- Released the pointer by pressing the button on the side of Blume-Lesos

- Looked at the tree tip, waited for a moment for the pointer to settle then pulled trigger

- Read the height directly from the 20 m scale

- Looked at the tree base and repeated above steps

- Based on results from above steps to decide total tree height:

 Added the 2 heights together of surveyors looked up to the tree tip in step 3 and down to the base in step 5

 Subtracted the height to the base from the height to the tree tip if surveyors were

on slopping ground and had to look up to both the tree tip and the tree base

Figure 12: Measured variable of tree (Manh Hung Bui, 2016)

Table 3: Table data collection

Figure 13: Scatter inventory plot location

Google earth explore (USGS)

It was used for download Landsat data from the previous time such as Landsat image data in 1997, 2001, 2007, 2013 and 2017

Microsoft Excel 2013

It basically was used for data entry of GPS location its conversion to a degree and track longitude and latitude to the ESRI shape file

Data collection

Image pre-processing

NDVI + Unsupervised classification

Accuracy

downloading Field survey data

The land cover will be classified into the following below:

1) Deciduous forest

2) Built up area, road, and Bare land

3) Water

4) Grassland

To Calculate NDVI (Normalized Difference Vegetation index) for Landsat 8

- Step 1: Calculating Reflectance Values

OLI spectral radiance data can also be converted to TOA planetary reflectance

using reflectance rescaling coefficients provided in the Landsat8 OLI metadata file The following equation is used to convert DN values to TOA reflectance for OLI image:

Example: (0.00002* “LC81270452016281LGN00_B1.TIF” – 0.1), Name as

Band_1

Where:

= TOA planetary reflectance, without correction for the solar angle Note that

 does not contain a correction for the sun angle

 = Band-specific multiplicative rescaling factor from the metadata

(Reflectance_Multi_Band_X, where X is the band number)

 = Band-specific additive rescaling factor from the metadata (Reflectance

_Add_Band_X, where X is the band number)

Q cal = Quantized and calibrated standard product pixel value (DN)[48]

- Step 2: Correcting the Reflectance value with sun angle

Reflectance with a correction for the sun angle is then:

cos SZ = sin SE

Where:

 = TOA planetary reflectance

SE = Local sun elevation angle The scene center sun elevation angle in degrees is

provided in metadata (Sun elevation)

SZ = Local solar zenith angle; SZ = 90o - SE[48]

To Calculate NDVI for Landsat 5

- Step 1: Calculating Reflectance Values (Conversion to TOA Radiance)

L ((LMAX - LMIN)/ (QCALMAX – QCALMIN)) * (QCAL – QCALMIN) + LMIN

Where: L = Spectral Radiance at the sensor’s aperture in watts/(meter squared *

ster * m)

QCAL = the quantized calibrated pixel value in DN

LMIN = the spectral radiance that is scaled to QCALMIN in watts/ (meter squared

Where:

 = Unitless planetary reflectance

L = Spectral radiance at the sensor’s aperture

d = Earth-Sun distance in astronomical units from an Excel file or interpolated

from values listed in Table

ESUN = mean solar exoatmospheric irradiances from table

s = Solar zenith angle in degrees [57]

Example:  = 3.1416 * “Band1_Radiance” * square (0.9982)/ (1970 * Cos(0.985)) For Landsat 8 and 5 we calculate the same formula of NDVI

For Landsat 8: Red is Band 4, NIR is Band 5, and SWIR is Band 6 and Band 7 For Landsat 5/7: Red is Band 3, NIR is Band 4, and SWIR is Band 5

To quantify the change in the extends of forest lost during the period of forest cover

in 1995-2005, 2005-2016 and 1995-2016

1) Data: Units: Meters, Cell size: 30x30 meters for 1995 and 2005

Table 4: Raster calculation for change detection

Forest 1997 Forest area in 1997 0= Non-forested,

Forest 2001 Forest area in 2001 0= Non-forested,

Forest 2001 Forest area in 2001

0= Non-forested

Forest 2007 Forest area in 2007 0= Non-forested

Forest 2007 Forest area on 2007 0= Non-forested,

Forest 2013 Forest area on 2013 0= Non-forested,

To quantify the total extents of forest changes during the period of 1995-2005 by using overlay approach

For Forest 1995: 1 refers to forest and 0 is no forest

For Forest 2005: 1 refers to forest and 0 is no forest

As overlaying, result is 0(0+0), 1 (0+1), 10(10+0), 11(10+1)

Step 1: Using the tool ArcGIS: Map Algebra and Raster Calculator

Raster Calculator: Forest 1995 + Forest 2005

0 = 0-No - Forest + 0 – No Forest stable, Clear

1 = 1- Forested + 0 – No Forest, Forest loss

10 = 0 – No Forest + 10 – Forested, Forest Gain

11 = 1 – Forested + 10 – Forested, Forest Stable

Step 2: Calculate the extents (area) of each land use type

Ha = (Count* 30*30)/10,000 (one hectare equals to 10,000 m2)

(One pixel equals to 30 x 30 m= 900 m2)

3.4.3 Allometric equation and Carbon stock estimation

To calculate the aboveground tree biomass, according to the ecological condition of tropical forest a biomass equation developed for tropical forest suggested by [28]

AGB= 0.0673 x (DBH 2 H) 0.976

Where,

The biomass sum of all individual biomass weights (in kilograms) by the area of the sampling plot (500 m2) This AGB value was changed to tones per hectare by multiplying

by 20 Later, biomass value was converted into carbon stock upon multiplying by the

conversion factor or default carbon fraction of 0.47 [58]

Table 5: Wood-specific density value of different tree species

Density