Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object Based Classification from THAICHOTE Satellite Data

Keywords: Carbon stock estimation, Object based classification, Para rubber plantation, THAICHOTE satellite, Mae num Prasae watershed/East Thailand Carbon stock estimation, Object based classification, Para rubber plantation, THAICHOTE satellite, Mae Num Prasae watershed/East Thailand

CO2 is the most abundant atmospheric gas related to the global warming. CO2 is responsible for more than half of the radiative forces associated with the greenhouse effect. The forest plantation could play an important role in carbon budget and thus be part of the Clean Development Mechanism (CDM) of the Kyoto Protocol. Para rubber (Hevea brasiliensis) is a perennial tree of economic importance in Indonesia, Malaysia and particularly in Thailand for producing latex to the world-market. The Para rubber has high biomass, high growth rate and strong potential of carbon storage. This study explored to the improve efficiency of measurements of carbon stock by remote sensing techniques on Para rubber plantations in East Thailand. Current methods of carbon stock estimations use classical pixel based classification on middle-resolution images and thus produce results with a large uncertainty. In this study, the method use very high resolution images from the THAICHOTE satellite, associated to field measurements to estimates the carbon stock and its evolution in the Mae num Prasae watershed. Using object based classifications, the plantations have been mapped and their age and girth have been estimated from a parametric model derived from spectral, textural, 3D information and field data. The results of this study show that these data can be used to map Para rubber plantation and distinguish age classes of trees in the plantations. The study propose that textural information is more useful than spectral information to capture tree canopy architecture and thus the age of the canopy. One spectral of Global Environment Monitoring (GEMI) and four textural information of Homogeneity, Dissimilarity, Contrast and Variance were used in the fit model for estimating the Para rubber tree girth and age while the 3D information (canopy height model: CHM) was not appropriated to build the image classification information. The model equation has been obtained from multiple linear regression analysis with correlation coefficient of R 2 =0.87 and thus can be used with confidence on the study area. Around 154 km 2 of the 232 km 2 of the studied area are covered by Para rubber plantations. The class of age of each plantation has been estimated to be 33% of the crop Doctor of Philosophy -Kitsanai Charoenjit iv Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object Based Classification from THAICHOTE Satellite Data surface belong to the young class (from 4 to 12 years), 34% of the crop surface belong to the middle class (from 12 to 18 years) and 33% belong to the old class (older than 18 years). The total amount of biomass and carbon stocks are 2.23 Megatons and 0.99 Megatons C respectively with uncertainty of 11%. In 2011, the total area sequestered 121 tCO2 by Para rubber plantations. Such a value is two orders of magnitude higher than carbon sequestered in the ocean.

LIST OF TABLES (Continue)

LIST OF FIGURES (continue)

xiii

LIST OF FIGURES (continue)

DEFINITION OF TERMS

Allometric equation -the forest-biomass inventory technique for dry biomass determination. It has been developed by establishing a relationship between the various physical parameters of the trees such as the diameter at breast height, height of the tree trunk, branch, total height of the tree, crown diameter, tree species [START_REF] Picard | Manual for Building Tree Volume and Biomass Allometric Equations[END_REF].

Biomass -the total amount of living organic matter in trees expressed as oven dry tons per unit area [START_REF] Brown | Estimating biomass and biomass change of tropical forests: a primer[END_REF].

Forest plantation -a forest established by planting or seeding in the process of afforestation and reforestation consisting of introduce or indigenous species [START_REF]State of the World's Forests." Food and Agriculture Organization of the United Nations[END_REF].

Carbon sequestration -the process of increasing the carbon content of a carbon pool [START_REF]State of the World's Forests." Food and Agriculture Organization of the United Nations[END_REF]. In the study, carbon sequestered by each age class was the carbon density or carbon stored in the biomass of Para rubber tree over its age, in unit tC ha -1 yr -1 .

Carbon stock -the absolute quantity of carbon held within a pool at a specified time [START_REF]State of the World's Forests." Food and Agriculture Organization of the United Nations[END_REF]. In the study carbon stock is use to imply the amount of carbon stored by Para rubber plantation.

Object based image analysis (OBIA) -it is a state-of-the-art of remote sensing classification techniques on very high resolution image. The advantage of a spatial resolution with pixel sizes significantly smaller than the average size of the object of interest comes with the disadvantage of an abundance of spatial detail and the accordingly huge amount of data to be processed. To overcome this drawback, the OBIA approach has proven to be an alternative to the pixel based image analysis and a large number of publications suggest that better results can be expected [START_REF]Object-Based Image Analysis Spatial Concepts for Knowledge-Driven Remote Sensing Applications Series[END_REF]. CO2 is the most abundant atmospheric gas related to the global warming. CO2 is responsible for more than half of the radiative forces associated with the greenhouse effect [START_REF] Sales | Carbon storage and sequestration potential of smallholder tree farms on Leyte Island, the Philippines[END_REF]. Forest may play an important role in the short carbon dioxide cycle. In particular tropical forests have the potential capacity to sequester and to conserve carbon permanently [START_REF] Backeus | A model for regional analysis of carbon sequestration and timber production[END_REF][START_REF] Sedjo | Forest carbon sequestration: some issues for forest investment[END_REF].

This is the reason why the Clean Development Mechanism recommended by the Kyoto

Protocol advocates evaluating tree capacity of CO2 storage in humid tropical forest plantations [START_REF]United Nation Framework Convention on Climate Change, Kyoto Protocol[END_REF]. Para rubber is a perennial tree of economic importance in Indonesia, Malaysia and particularly in Thailand for producing latex to the world-market [START_REF]Production of Natural Rubber[END_REF]. The Para rubber has high biomass, high growth rate and strong potential of carbon storage [START_REF] Viriyabuncha | Handbook of Stand Biomass Estimation[END_REF][START_REF] Chantuma | Rubber new planting in Thailand: towards the world affected on climate change[END_REF].

Today, very high resolution (VHR) sensors on board of satellites can map tropical forest plantations providing valuable data to evaluate forest biomass and carbon stocks evolution. VHR data has overcome the limitation of spatial resolution of the medium-low resolution sensors such as Landsat 8 OLI (15 m) or MODIS (250 m). These sensors cannot capture the tree characteristics such as crown canopy. Therefore, estimation of plantation biomass and carbon stocks from low resolution remote sensing data was inaccurate [START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF].

THAICHOTE satellite camera (previously named THEOS) is a high resolution sensor on 2 m resolution. It is the first Earth observation satellite of Thailand. Its data are potentially an important data source for biomass and carbon stocks estimation on large surfaces.

However, the monitoring of forest carbon stock is a complex task. Numerous approaches have been proposed to estimate biomass [START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF][START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF][START_REF] Fuchs | Estimating aboveground carbon in a catchment of the Siberian forest tundra:combining satellite imagery and field inventory[END_REF][START_REF] Lu | Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin[END_REF][START_REF] Iglesias | Determination of carbon sequestration and storage capacity of eucalyptus plantation in Sra Kaew Province[END_REF][START_REF] Nilubol | Geo-Informatics Technology for Age Class Identification for Para rubber Plantation in Krabi Province[END_REF][START_REF] Noiruksar | Application of Remote Sensing for Aboveground Carbon Storage Determination of Teak in Khun Mae Kham Mee Plantation, Prae province[END_REF][START_REF] Zheng | Combining remote sensing imagery and forest age inventory for biomass mapping[END_REF]. The spectral information contained in satellite data is classically used [START_REF] Lu | Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin[END_REF][START_REF] Iglesias | Determination of carbon sequestration and storage capacity of eucalyptus plantation in Sra Kaew Province[END_REF][START_REF] Nilubol | Geo-Informatics Technology for Age Class Identification for Para rubber Plantation in Krabi Province[END_REF][START_REF] Noiruksar | Application of Remote Sensing for Aboveground Carbon Storage Determination of Teak in Khun Mae Kham Mee Plantation, Prae province[END_REF][START_REF] Zheng | Combining remote sensing imagery and forest age inventory for biomass mapping[END_REF]. The methods developed with these data

Doctor of Philosophy -Kitsanai Charoenjit

Introduction/ 2

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data cannot differentiate the biomass according to the tree species or tree age [START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF][START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF][START_REF] Fuchs | Estimating aboveground carbon in a catchment of the Siberian forest tundra:combining satellite imagery and field inventory[END_REF]. Recently, methods based on the texture measurement were developed to obtain better results than using only spectral information [START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF][START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF][START_REF] Fuchs | Estimating aboveground carbon in a catchment of the Siberian forest tundra:combining satellite imagery and field inventory[END_REF]. Field data and remote sensing data were linked using common regression analysis [START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF][START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF], with most studies relating visible and near-infrared (NIR)

wave range, vegetation indices and texture analysis.

Remote sensing Classification Problems on Forest Biomass Estimation

This study have identified two major problems in biomass estimation by remote sensing classification techniques. The first problem is the noise in the image classification.

Image noise is a critical problem of remote sensing classification on VHR data. The result is inaccurate in terms of geometry of class boundary (or plantation limit) and the age data of Para rubber plantation. The legacy technique is derived from supervised and unsupervised methods for assigning a class label to individual pixel based on distance or similarity measures in feature space [START_REF] Arko | Uncertainties in segmentation and their visualisation application of geoinformatics[END_REF]. This approach has been used simple spectral information in order to class identification. By contrast, the object based classification (object based image analysis: OBIA) overcome limitation from pixel based classification. Figure 1-1(c) revised the Para rubber plantation mapping derived from OBIA approach. OBIA process is done in two steps: image segmentation and modeling for object identification.

Segmentation in remote sensing can remove image noise while the model can identify the object by analyzing more information such as reflectance distribution, shape, size, and texture [START_REF] Arko | Uncertainties in segmentation and their visualisation application of geoinformatics[END_REF].

The second problem is the poor-quality relationship between field data and VHR data when using spectral information for biomass estimation. Recently, methods based on the texture measurement were developed to obtain better results than those using only spectral [START_REF] Lu | Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin[END_REF][START_REF] Iglesias | Determination of carbon sequestration and storage capacity of eucalyptus plantation in Sra Kaew Province[END_REF][START_REF] Nilubol | Geo-Informatics Technology for Age Class Identification for Para rubber Plantation in Krabi Province[END_REF][START_REF] Noiruksar | Application of Remote Sensing for Aboveground Carbon Storage Determination of Teak in Khun Mae Kham Mee Plantation, Prae province[END_REF][START_REF] Zheng | Combining remote sensing imagery and forest age inventory for biomass mapping[END_REF]. Consequently, the established relationships between field data and remote sensing data were weak.

State-of-the-art of forest biomass estimation at large-surface

The Figure 1-2 shows a brief story of the state of the art of forest biomass estimation at large-surface estimation which the ground forest biomass estimation and remote sensing data via optical satellite are purposed. The Ground forest biomass inventory used the destructive method involves harvesting of all the trees in the known area and measuring the weight of the different components of the harvested tree like the tree trunk, leaves and branches root and measuring the weight of these components after they are oven dried. Therefore, the destructive method has been addressed by the tree allometry. The allometric equation is developed by establishing a relationship between the various physical parameters of the trees such as the diameter at breast height, height of the tree trunk, branch, total height of the tree, crown diameter and tree species [START_REF] Picard | Manual for Building Tree Volume and Biomass Allometric Equations[END_REF].

Despite, the Ground forest biomass method provided accurately the forest biomass estimation but this method is limited to estimate biomass on large-surface analysis, using more time and resource consuming. Elevation Satellite. The study shown a high-resolution approach to map forest aboveground biomass at regional-to-continental scales and assess sources of uncertainties in the estimates.

•

Objectives of the Study

The goal of this study is to improve the Para rubber biomass and carbon stocks estimation of estimation using object-based classification combining both spectral and textural information from a THAICHOTE satellite image that was acquired in December 2011 over the Mae num Prasae watershed, Thailand. The objective points can be written in below:

• To develop the empirical model for identification of Para rubber tree girth and age using linear multiple regression on both spectral and textural information from THAICHOTE satellite data.

• To identify the Para rubber plantation age class map using Object based classification from THAICHOTE satellite data.

• To estimates the biomass and carbon stock in Para rubber plantations over the Ma num Prasae watershed, East Thailand.

Doctor of Philosophy -Kitsanai Charoenjit

The Study Limitation

• Biomass-allometric equation and default value of carbon conversion is applied to estimate biomass and carbon stocks of Para rubber clones RRIM 600 (Commercial rubber tree breed was developed by Rubber Research Institute of Malaysia No.600).

• The THAICHOTE optical satellite data 2011 is used in the major source of the study. 2011). This province is 7 th order of Thailand for latex production performancing [START_REF]Production of Natural Rubber[END_REF]. The Mae num Prasae watershed is shown in Figure 123. % with moderate wind, and bright sunshine for about 2,000 hours in a year [START_REF] Rao | Climatic requirements[END_REF].

Conceptual Framework

Para rubber productivity

Para rubber is a perennial tree of economic importance in Indonesia, Malaysia and particularly in Thailand for producing latex to the world-market. The Para rubber has high biomass, high growth rate and strong potential of carbon storage [START_REF] Viriyabuncha | Handbook of Stand Biomass Estimation[END_REF][START_REF] Zhang | Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data[END_REF].Thailand is the leader of rubber producer in the world that produces around 3.5 million tons per year or 37% of the world annual production [START_REF]Production of Natural Rubber[END_REF]. The world product of Para rubber was listed in Table 2-1.

Forest carbon cycle

The forest C (carbon) cycle, the contribution made by woodland to the C balance at any scale depends on the rate at which CO2 is removed from the atmosphere and the quantity of C retained in the woodland. The C stocks in the forest include the above and below-ground components of trees, understorey and the soil. Trees take up CO2 from the atmosphere during photosynthesis following basic chemical equation (The tree use Sunlight + CO2 + Water and Nutrients). Much of the carbon that is taken up by the tree through photosynthesis is released again as CO2 through above-ground and root respiration, which provides the energy for tissue growth and maintenance.

The remaining carbon is partitioned between or allocated to leaf, root, seed, stem and branch biomass. Most of the carbon in trees is in the stem approximately 50%, with substantial amounts in branches and below ground in roots. Decomposition of these litter and root exudation components releases CO2. This flux of CO2 is often referred to as soil respiration, usually include the respiration of the roots and any soil invertebrates. During decomposition the organic carbon remains in a wide variety of different components of soil organic carbon (SOC). Soil microbial activity can also result in the net release of the greenhouse gases CH4 and N2O, although only the former is formally part of the carbon cycle.

However, the mass of carbon exchanged via methane fluxes is normally very much smaller than that in CO2 exchanges, so is usually ignored for C balances.

Several terms are conventionally used to represent the different CO2 fluxes in the system, the net primary productivity (NPP) is the difference between the gross primary productivity (GPP) and the respiration by the trees and other vegetation (autotrophic respiration, RA), and the net ecosystem productivity (NEP) is the difference between NPP and the respiration by animals and micro-organisms (heterotrophic respiration, RH). NEP is

Doctor of Philosophy -Kitsanai Charoenjit

Literature Review/ 18

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data therefore the rate of carbon accumulation in the system [START_REF] Morison | Understanding the carbon and greenhouse gas balance of forests in Britain[END_REF]. Forest carbon cycle is shown in The world's forests area of 3,500 million hectare constitute 30 percent of the total land area [START_REF]State of the World's Forests." Food and Agriculture Organization of the United Nations[END_REF]. The world's forests, 57 percent are mostly tropical located in developing countries [START_REF]State of the World's Forests." Food and Agriculture Organization of the United Nations[END_REF]. According to S. Brown, (1997) [START_REF] Brown | Estimating biomass and biomass change of tropical forests: a primer[END_REF] and GEO 3, (2003) [START_REF]Global Environment Outlook Three: Past, present and future perspective[END_REF], carbon pools in the world's forest are estimated at 348 Gt C in its vegetation and 478 Gt C in soil. The amount of carbon that can be conserved and sequestered by forest sector practices by 2050 is equivalent to 11 to 15 percent of the total fossil fuel emission [START_REF] Brown | Estimating biomass and biomass change of tropical forests: a primer[END_REF]. This is because trees are long-live plants that develop large biomass thereby capturing large amounts of carbon over a growth cycles of many decades [START_REF]State of the World's Forests." Food and Agriculture Organization of the United Nations[END_REF]. The mitigation potential of forests found in Asian countries as mentioned in the IPCC Report 2001 [START_REF]Change. Second Assessment Report[END_REF] is estimated at about 26.5 Gt carbon.

Ground forest biomass-carbon stock inventory

Woody biomass and wood volumes are important indicators that provide a comprehensive measure of the capacity a forest ecosystem to sequester carbon [START_REF]State of the World's Forests." Food and Agriculture Organization of the United Nations[END_REF] since it is assumed that about 50 percent of the biomass is carbon [START_REF] Brown | Estimating biomass and biomass change of tropical forests: a primer[END_REF]. As defined in Chapter II, biomass refers to the total amount of organic living matter in trees expressed as oven-dry tins per unit area. It represents the potential amount of carbon pools in forest land and that can be added to the atmosphere as carbon dioxide once the forest is burned.

The Ground biomass and carbon determination, Using destructive method involves harvesting of all the trees in the known area and measuring the weight of the different components of the harvested tree like the tree trunk, leaves and branches root and measuring the weight of these components after they are oven dried. The method has a limitation of a small area or small tree sample sizes. Although this method determines the biomass accurately for a particular area, it is time and resource consuming, strenuous, destructive, and it is not feasible for a large scale analysis. Then, the destructive method has been addressing by the use of biomass allometric equation. It has been developed by establishing a relationship between the various physical parameters of the trees such as the diameter at breast height, height of the tree trunk, branch, total height of the tree, crown diameter, tree species [START_REF] Picard | Manual for Building Tree Volume and Biomass Allometric Equations[END_REF]. The steps of allometric equation process were shown in Figure 23. The majority of the biomass equations are in the form Y = aX b , where Y is the weight of stem or branches or leaves (kg), X is the DBH 2 .Ht (cm 2 .m) and a, b are constant value of equation [START_REF] Kira | Primary production and turnover of organic matter in different ecosystems of the Western Pacific[END_REF]. The existing biomass equations were selected from the geographical regions closest to sample area.

Doctor of Philosophy -Kitsanai Charoenjit

Literature Review/ 20

Allometric equation for Hevea brasiliensis (clone RRIM 600)

Several allometric relationship method are used for biomass estimation in Hevea brasiliensis-rubber clone using girth and height parameters to inputting data. The confident of allometric equation is published in range between R 2 > 0.9. This Chapter II is descript the tree allometric equation published by Chantuma et al., 2004 as cite by ref. [START_REF] Chantuma | Rubber new planting in Thailand: towards the world affected on climate change[END_REF] for estimate a total weight biomass of Hevea brasiliensis-rubber clone RRIM 600. It was shown in Figure 234a relationship between 95 trees rubber girth and total biomass. The biomass equation declared relationship between total dry matter (branch, trunk, leaf and roots) and girth of trees: Y = 0.0082X

Carbon concentration of Hevea brasiliensis (clone RRIM 600)

The average 44.52 percentage of Carbon concentration of RRIM 600 biomass was found by the study of Chantuma et al., 2004 [7] where measured by auto analyzer equipment (Table 2-2). The maximum of Carbon concentration is shown in Seed kernel endosperm part (57.27%) while the minimum is shown in Bark tap root part (39.45%).

General information of remote sensing used in this study

As defined in Chapter II, the remote sensing techniques are realized to the optical satellite data. Remote sensing is the measurement or acquisition of information of some property of an object or phenomenon, by a recording device that is not in physical or intimate contact with the object or phenomenon under study [START_REF]Colwell, Manual of Remote Sensing[END_REF]. Various land cover absorbs and emits energy, which is recorded by sensors in the remote sensing device. The energy emitted by land covers called electromagnetic radiation differs among land covers giving certain features of the earth distinguishable characteristics as recorded in the satellite image.

Remote sensing using electromagnetic radiation has four components: a source, interactions with the earth's surface, interaction with the atmosphere and sensor [START_REF] Curran | Principles of Remote Sensing[END_REF].

Electromagnetic energy reaching the earth's surface from the sun is reflected, transmitted or absorbed. Reflected energy travels upwards thought, and interacts with, the atmosphere; that the part of it which enters the field of view of the sensors is detected and converted into a numerical value that transmitted to a ground receiving station on the earth.

The characteristics of imaging remote sensing instrument can be summarized in term of their spatial, spectral, and radiometric resolutions. The spatial resolution is the ability to measure property (instantaneous field of view (IFOV)). The IFOV is defined as the area on

Doctor of Philosophy -Kitsanai Charoenjit

Literature Review/ 23

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data the ground. The term of spectral resolution refers to the width of these spectral bands measured in micrometers (µm) or nanometers (nm). The width and numbers of spectral bands determine the degree of discriminating power. The third important property, radiometric resolution refers to the number of digital quantization levels used to express the data collected by sensor. The greater the number of quantization levels the greater the detail in the information collected by the sensor. The different solutions of an earth observing system should be selected with the aim of investigation in mind [START_REF] Mather | Computer processing of remotely-sensed images: an introduction[END_REF].

THAICHOTE satellite data

Remote sensing of vegetation

Plants have adapted their internal and external structure to perform photosynthesis.

This structure and its interaction with electromagnetic energy have a direct impact on how leave and canopies appear spectrally when recorded using remote sensing instruments [START_REF] Jensen | Remote sensing of environment: an earth resource perspective[END_REF].

Chlorophyll is the most important plant pigment absorbing blue and red light. Thus, the spectral reflectance curve rises in green wave bands. In addition, there are many factors that cause difference spectral such as percent canopy closure, temporal characteristics, managed phonological cycles, etc.

Broadband greenness Vegetation indexes

NIR -RED NIR + RED

(2-1)

• Atmospherically Resistant Vegetation Index (ARVI)

The Atmospherically Resistant Vegetation Index: ARVI [START_REF] Kaufman | Atmospherically resistant vegetation index (ARVI) for EOSMODIS[END_REF] is image enhancing according to the factor resistant to the atmospheric (eg. aerosol). The result of this index ranges from -1 to 1. The common range for green vegetation is 0.2 to 0.8. The ARVI is calculated using Equation (2-2).

NIR -2 RED -BLUE NIR + 2 RED -BLUE

(2-2)

• Global Environment Monitoring Index (GEMI)

The Global Environment Monitoring Index: GEMI [START_REF] Pinty | GEMI: a non-linear index to monitor global vegetation from satellites[END_REF] is eliminated the need for a detailed atmospheric correction by constructing a stocked atmospheric correction for the vegetation index. The result of GEMI is shown in range from -1 to 1. The GEMI is calculated using Equation (2-3).

. .

where : n = [ ! .

(2-3)

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand

Using Object-based Classification from THAICHOTE Satellite Data

• Infrared Percentage Vegetation Index (IPVI)

The Infrared Percentage Vegetation Index : IPVI [START_REF] Crippen | Calculating the vegetation index faster[END_REF] this index as a way of improving calculation speed. It also is restricted to positive values between 0 and 1, which eliminates the need for storing a sign for the vegetation index values, and it eliminates the conceptual strangeness of negative values for vegetation indices. The IPVI is calculated using Equation (2-4).

NIR NIR + RED

(2-4)

• Modified Soil Adjusted Vegetation Index 2 (MSAVI 2)
The Modified Soil Adjusted Vegetation Index 2 : MSAVI 2 [START_REF] Qi | Modified soil adjusted vegetation index (MSAVI)[END_REF] is adjusted vegetation indices that seek to address some of the limitation of NDVI when applied to areas with a high degree of exposed soil surface. The value of this index ranges from -1 to 1. The MSAVI2 is calculated using Equation (2-5).

2NIR + 1 -# 2NIR + 1 -8 NIR -RED 2

(2-5)

where: RED = the THAICHOTE red band (0.62-0.69 µm) NIR = the THAICHOTE near infrared band (0.77-0.90 µm)

Texture measurement

The texture of an image is related to the statistical characteristics of association of pixels at a given scale. The texture of an image is a good descriptor of the forest canopy [START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF][START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF]. The Grey-Level Co-occurrence Matrix (GLCM) texture measurement [START_REF] Haralick | Textural features for image classification[END_REF][START_REF] Anys | Texture analysis for the mapping of urban areas using airborne MEIS-II images[END_REF] The first one is the generation of the co-occurrence matrix called probability matrix, and the second one is the computation of texture descriptors. The probability matrix characterizes the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, creating a GLCM, and then extracting statistical measures from this matrix [START_REF] Anys | Texture analysis for the mapping of urban areas using airborne MEIS-II images[END_REF]. A sliding window was used to generate co-occurrence matrix. Four directions (0°, 45°, 90°, and 135°) of pixel neighborhood connections were considered. The probability matrix generation was illustrated in Figure 23456.

Second one, the texture descriptors were computed from GCLM. The Haralic et al.

[42] equations were used for texture measurements. These equations refer to 3 groups of descriptors that are: the contrast group (Contras, Dissimilarity, and Homogeneity), the orderliness group (Angular second moment, Entropy) and the descriptive statistics group (Mean, Variance, and Correlation), (Table 234). Eight textural layers have been computed

from the original image. total is 16 elements (sliding window 4x4 pixels). Frame work matrix was used to fill neighbor position (i,j). Twice in the original image the reference pixel is 0 and its eastern neighbor is also 0. Twice the reference pixel is 0 and its eastern neighbor is 1. Three times the reference pixel is 2 and its neighbor is also 2. The probability was computed which divide by sum of total elements of window.

0 0 1 1 0 0 1 1 0 2 2 2 2 2 3 3
Neighbor pixel value / Reference pixel value

0 1 2 3 0 0,0 0,1 0,2 0,3 1 1,0 1,1 1,2 1,3 2 2,0 2,1 2,2 2,3 3 3,0 3,1 3,2 3,3 2
% >@==?A<B1@ = ( C i -μ 1 j -μ 2 # σi + σj E 1,2/
Measure of the linear dependency between neighbouring pixels.

where:

Pi,j = The probability matrix i = Reference pixel j = Neighborhood pixel µi, µj, σi , σj = The mean and standard deviation of Pi, j respectively

3D terrain model 2.4.4.1 A simple stereo system

A brief story of a simple stereo system is descripted [START_REF] Rawlinson | Design and Implementation of a Spatially Enabled Panoramic Virtual Reality Prototype[END_REF].The computation of object positions in space requires linking coordinates of points in 3-dimensional space with coordinates of their corresponding images points. Using the example of a simple stereo system (Figure 234567) shown from a top down perspective, the determination of the position of P and Q in space can easily be seen through the process of triangulation.

Doctor of Philosophy -Kitsanai Charoenjit

Literature Review/ 32

N O = J O OM G P (2-7) J O = F OM G P -G Q L O = J O OM R

Implementation of a 3D terrain model on computer simulation

The Exelis Visual Information Solutions [START_REF]The Exelis Visual Information Solutions product documentation center[END_REF] has been explained the 3D terrain generation using stereo data. There are three steps in creating a 3D terrain model that are crucial to generating acceptable results: epipolar image creation, image matching, and 3D terrain geocoding. (along epipolar lines) between the left and right image is stored and is used to build the 3D terrain model. Thus, the quality of image matching largely determines the quality of the output 3D terrain.

• 3D terrain Geocoding: 3D terrain geocoding reprojects the 3D terrain model from the epipolar projection to an output map projection and units that you specify. This step involves filling failed pixels and resampling to a pixel spacing that you specify.

Object Based Image Analysis

The advantage of a spatial resolution with pixel sizes significantly smaller than the average size of the object of interest comes with the disadvantage of an abundance of spatial detail and the accordingly huge amount of data to be processed. To overcome this drawback, the object based image analysis (OBIA) approach has proven to be an alternative to the pixel based image analysis and a large number of publications suggest that better results can be expected [START_REF]Object-Based Image Analysis Spatial Concepts for Knowledge-Driven Remote Sensing Applications Series[END_REF]. The object-based approach suggests a two-staged approach. In the first step pixels are merged to object clusters, possibly in a multi-level object hierarchy, which then will be analyzed and classified in the second step.

workflow

The workflow described here starts with object primitives as well. However, in contrast to the object-based workflow, it uses these objects not only as information carriers but also as building blocks for any further shape modification, merging, or segmentation procedures. In a whole sequence of processing steps, where segmentation steps alternate with evaluation and classification steps, these object primitives are constantly altered until they become the desired objects of interest. Because this strategy aims for correct shaping and classification of objects, and objects are also used at every step during the procedure as the central processing unit, serving both as information provider and as building blocks, this workflow consequently can be called "object-oriented image analysis".

Doctor of Philosophy -Kitsanai Charoenjit

Literature Review/ 36

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data This workflow can be described best with a spiral and is illustrated in Figure 23456789. The entire process is iterative and starts in the first step with the creation of object primitives using any (knowledge-free) segmentation algorithm. The next step uses the object primitives in order to perform a first evaluation and classification, thus introducing semantics. Building on this result, the subsequent step allows refinement or improvement of the segmentation locally for a specific class. Thus, the whole process alternates iteratively between local object modification on the one hand and local object evaluation and classification on the other. By using such an iterative approach, different object classes can be addressed with different object modification strategies. During this process, the objects are altered from stage to stage until they represent the final target objects. Only the combination of correct shaping and correct classification characterizes the target objects; otherwise the results will be insufficient.

The spiral in Figure 2-9 represents this alternation between segmentation and classification in the object-oriented approach.

As the analysis progresses, classification detail and accuracy are growing together with segmentation detail and accuracy. Whereas the process starts with rather simple and knowledge-free segmentation steps, more and more expert and domain knowledge is introduced and used in later steps. The more closely the process approximates the final target objects, the higher is the abstraction from the original image information [START_REF]Object-Based Image Analysis Spatial Concepts for Knowledge-Driven Remote Sensing Applications Series[END_REF].

Study Process

The study process was defined into 4 board parts (Figure 3-1). Firstly, the Field Data (Part 1) was collected using Forest inventory and Ground Biomass-Carbon estimation.

Secondly, the remote sensing data obtained from THAICHOTE satellite data was applied to mapping of Para rubber plantation surface. Using 3 steps: Satellite Image Preprocessing (Part 2), Image Classification (Part 3) and Para rubber Surface Extraction (Part 4). The biomass and carbon stock of single trees were linked to the remote sensing data using linear multiple regression model. Finally, the total biomass and carbon stock at large surface were estimated.

Field data

Field measurements

The study was applied the forest inventory technique (an equipment shown in Figure 3-2). In the field, diameter at breast height (DBH), tree height (see in appendix A1 for tree height measurement), girth (circulation) and age were measured on about 10 to 15 trees per plot (Figure 3

Satellite Image Correction

On December 27, 2011 at 03:22 GMT during the dry season, THAICHOTE image was acquired where cloud cover was less than 10%. The image was first pan-sharpened and georeferenced using ortho-rectification method [START_REF] Jacek | Block adjustment of high-resolution satellite images described by rational polynomials[END_REF]

Image Classification

Spectral information

Five classical of broadband greenness Vegetation Indexes were calculated from the 4 spectral bands. Each index is a combination of the various bands ratios as follows:

• Atmospherically Resistant Vegetation Index (ARVI) [START_REF] Kaufman | Atmospherically resistant vegetation index (ARVI) for EOSMODIS[END_REF] was determined using the Equation (3-6):

->? -?A, -/&BA ->? + ?A, -/&BA

• Infrared Percentage Vegetation Index (IPVI) [START_REF] Crippen | Calculating the vegetation index faster[END_REF] was determined using the Equation (3-8):

->? ->? + ?A, (3-8)

•

Modified Soil Adjusted Vegetation Index2 (MSAVI2) [START_REF] Qi | Modified soil adjusted vegetation index (MSAVI)[END_REF] was determined using the Equation (3-9):

->? + ! -G ->? + ! -->? -?A,

• Normalized Difference Vegetation Index (NDVI) [START_REF] Kaufman | Atmospherically resistant vegetation index (ARVI) for EOSMODIS[END_REF] was determined using the Equation (3-10):

->? -?A, ->? + ?A,

Textural information

The texture of an image is related to the statistical characteristics of association of pixels at a given scale. The texture of an image is a good descriptor of the forest canopy [START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF][START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF]. The Grey-Level Co-occurrence Matrix (GLCM) texture measurement [START_REF] Haralick | Textural features for image classification[END_REF][START_REF] Anys | Texture analysis for the mapping of urban areas using airborne MEIS-II images[END_REF] was applied to THAICHOTE panchromatic image. The texture information illustrated in Figure 345.

Figure 3-5

The processes of textural information.

The GLCM is a tabulation of how often different combinations of pixel brightness values (grey levels) occur in the image. The GLCM is realized in 2 steps, the first one is the generation of the co-occurrence matrix called probability matrix, and the second one is the computation of texture descriptors. The probability matrix characterizes the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, creating a GLCM, and then extracting statistical measures from this matrix [START_REF] Anys | Texture analysis for the mapping of urban areas using airborne MEIS-II images[END_REF]. A 15x15 pixels sliding window [START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF]

Three dimensional (3D) information

The estimation of Para rubber tree height was obtained from the Canopy Height Firstly, the epipolar image was generated for setting the geometrical constraint between two frame images of a THAICHOTE stereo pair data. The Rational Polynomial Coefficients (RPCs) [START_REF] Jacek | Block adjustment of high-resolution satellite images described by rational polynomials[END_REF] and tie points obtained from the scale invariant feature transformation (SIFT) [START_REF] Lowe | Distinctive image features from scale-invariant keypoints[END_REF] were used for aligning the image orientation. SIFT was used for automatic tile point matching of the stereo pair data The input parameters obtained from the THAICHOTE header file (sensor's model) were shown in Secondly, point cloud generation and image geocoding were used for the Digital Surface Model (DSM) generation. The point clouds (called depth information) were computed using the parallax equation [START_REF] Wolf | Elements of Photogrammetry[END_REF]. Then, the DSM surface was interpolated using triangulated irregular network (TIN).

Finally, the CHM was generated from the different of height surface of Digital Surface Model (DSM) and Digital Terrain Model (DTM) was computed using the Equation The DTM was generated using the random point cloud (x, y, z) on the bare-ground surface. Without the areas of vegetation and human artifact, the inverse of Normalize Difference Vegetation Index (NDVI) was applied for extracting the point cloud. This process, the sparse points cloud was obtained. Therefore, the DTM surface was interpolated using inverse distance weighting method (IDW) [START_REF] Watson | A Refinement of Inverse Distance Weighted Interpolation[END_REF].

Mask information

The study used the NSDI (Thai National Spatial Data Infrastructure) GIS database 2011 obtained by the Geo-Informatics and Space Technology Development Agency (GISTDA, Thailand) to extract the Para rubber plantation areas and evaluate the areas not concerned by the study that the processes are: (1) the urban areas, other agricultural, natural forest areas and road were selected for build mask information, (2) the mask database was map referenced to the UTM WGS84, Z48, (3) the localization of bare soil and water bodies was evaluated by inverse of Normalized Different vegetation Index (NDVI). These areas were masked on the THAICHOTE pan-sharpened image.

Doctor of Philosophy -Kitsanai Charoenjit

Data & Methods/ 55

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Para rubber plantation Surface Extraction

The Object Based Image Analysis (OBIA) was used to classify the Rubber tree canopy properties (eg. reflectance data, texture data). Two results were extracted from image classification and modeling which are a map of the plantations and an estimation of girth and age of trees of each plantation. The map of plantation was realized using an image segmentation process. The girth and age of trees of each plantation were estimated from a linear regression equation build from the relationships between field data and image characteristics measured at the emplacement where field data were acquired. Finally, the results of biomass and carbon stock estimation over the Mae num Prasae watershed were calculated using the integration of Para rubber plantation surface map, Field data and Allometric equation. The Para rubber Surface Extraction illustrated in Figure 34567. This study process are descripted as follows:

Image Segmentation

Para rubber plantation limits (plantation boundaries) were extracted automatically from image multi-scale segmentation [START_REF] Guigues | Scale sets image analysis[END_REF][START_REF] Bris | Extraction of vineyards out of aerial ortho-image using texture information[END_REF] on THAICHOTE data. The results of multi scale segmentation were tested by empirically visualization [START_REF] Cohen | A coefficient of agreement for nominal scales[END_REF] to find a suitable compromise The uncertainty data of Para rubber surface (plantation limit) was computed using the comparison of limit between manual digitization and multi-scale segmentation.

Doctor of Philosophy -Kitsanai Charoenjit

Para rubber Age class and Girth Estimation

Values of the various vegetation and textural indexes were extracted on the position of each plot measured in the field. From that data set using a multiple linear regression model, a set of linear equations relating the girth of tree and the characteristics of the layers was extracted in the form of the Tree Girth Model (called TGM). The confidence in this equation has been analyzed using Pearson's correlation coefficient. TGM was generated using linear multiple regression stepwise method for predicting tree girth and tree age. The tree girth is a function from multi-parameterization of spectral and textural information. These parameters was applied the mean value of each plot using GIS zonal operation.

The input dataset was shown in Appendix A.3.

The Equation [START_REF] Sedjo | Forest carbon sequestration: some issues for forest investment[END_REF][START_REF]United Nation Framework Convention on Climate Change, Kyoto Protocol[END_REF][START_REF]Production of Natural Rubber[END_REF][START_REF] Viriyabuncha | Handbook of Stand Biomass Estimation[END_REF][START_REF] Chantuma | Rubber new planting in Thailand: towards the world affected on climate change[END_REF][START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF][START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF][START_REF] Fuchs | Estimating aboveground carbon in a catchment of the Siberian forest tundra:combining satellite imagery and field inventory[END_REF][START_REF] Lu | Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin[END_REF][START_REF] Iglesias | Determination of carbon sequestration and storage capacity of eucalyptus plantation in Sra Kaew Province[END_REF][START_REF] Nilubol | Geo-Informatics Technology for Age Class Identification for Para rubber Plantation in Krabi Province[END_REF][START_REF] Noiruksar | Application of Remote Sensing for Aboveground Carbon Storage Determination of Teak in Khun Mae Kham Mee Plantation, Prae province[END_REF] was used to estimate the girth of trees of each plantation delineated at the segmentation step. Knowing the surface of the plantations and the age of the plantation, the biomass of each plantation was calculated from the allometric equations (equation 3-1). Carbon stock was estimated using the conversion factor and carbon sequestration of Equations (3-2, 3-3).

The root mean square error (RMSE) and mean absolute percentage error (MAPE)

were used to evaluate the model accuracy. These statistics was shown to Equation [START_REF] Sedjo | Forest carbon sequestration: some issues for forest investment[END_REF][START_REF]United Nation Framework Convention on Climate Change, Kyoto Protocol[END_REF][START_REF]Production of Natural Rubber[END_REF][START_REF] Viriyabuncha | Handbook of Stand Biomass Estimation[END_REF][START_REF] Chantuma | Rubber new planting in Thailand: towards the world affected on climate change[END_REF][START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF][START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF][START_REF] Fuchs | Estimating aboveground carbon in a catchment of the Siberian forest tundra:combining satellite imagery and field inventory[END_REF][START_REF] Lu | Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin[END_REF][START_REF] Iglesias | Determination of carbon sequestration and storage capacity of eucalyptus plantation in Sra Kaew Province[END_REF][START_REF] Nilubol | Geo-Informatics Technology for Age Class Identification for Para rubber Plantation in Krabi Province[END_REF][START_REF] Noiruksar | Application of Remote Sensing for Aboveground Carbon Storage Determination of Teak in Khun Mae Kham Mee Plantation, Prae province[END_REF][START_REF] Zheng | Combining remote sensing imagery and forest age inventory for biomass mapping[END_REF][START_REF] Sedjo | Forest carbon sequestration: some issues for forest investment[END_REF][START_REF]United Nation Framework Convention on Climate Change, Kyoto Protocol[END_REF][START_REF]Production of Natural Rubber[END_REF][START_REF] Viriyabuncha | Handbook of Stand Biomass Estimation[END_REF][START_REF] Chantuma | Rubber new planting in Thailand: towards the world affected on climate change[END_REF][START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF][START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF][START_REF] Fuchs | Estimating aboveground carbon in a catchment of the Siberian forest tundra:combining satellite imagery and field inventory[END_REF][START_REF] Lu | Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin[END_REF][START_REF] Iglesias | Determination of carbon sequestration and storage capacity of eucalyptus plantation in Sra Kaew Province[END_REF][START_REF] Nilubol | Geo-Informatics Technology for Age Class Identification for Para rubber Plantation in Krabi Province[END_REF][START_REF] Noiruksar | Application of Remote Sensing for Aboveground Carbon Storage Determination of Teak in Khun Mae Kham Mee Plantation, Prae province[END_REF][START_REF] Zheng | Combining remote sensing imagery and forest age inventory for biomass mapping[END_REF][START_REF] Arko | Uncertainties in segmentation and their visualisation application of geoinformatics[END_REF]. Additionally, the uncertainties data of the TGM was computed using root mean square error (RMSE), (Equation 3-15). Therefore, the total uncertainty of biomass and carbon stock estimation were combined using the uncertainty of Para rubber surface and the uncertainty of TGM.

• Root Mean Square Error (RMSE)

n 98 = o J p -q r

Accuracy Assessment

The confusion matrix and kappa index [START_REF] Cohen | A coefficient of agreement for nominal scales[END_REF] were employed to determine the accuracy of Para rubber tree age class map obtained by object based classification. The accuracy assessment manipulated using the combination of reference data and predict data. The confusion matrix and kappa index were shown in equation [START_REF] Sedjo | Forest carbon sequestration: some issues for forest investment[END_REF][START_REF]United Nation Framework Convention on Climate Change, Kyoto Protocol[END_REF][START_REF]Production of Natural Rubber[END_REF][START_REF] Viriyabuncha | Handbook of Stand Biomass Estimation[END_REF][START_REF] Chantuma | Rubber new planting in Thailand: towards the world affected on climate change[END_REF][START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF][START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF][START_REF] Fuchs | Estimating aboveground carbon in a catchment of the Siberian forest tundra:combining satellite imagery and field inventory[END_REF][START_REF] Lu | Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin[END_REF][START_REF] Iglesias | Determination of carbon sequestration and storage capacity of eucalyptus plantation in Sra Kaew Province[END_REF][START_REF] Nilubol | Geo-Informatics Technology for Age Class Identification for Para rubber Plantation in Krabi Province[END_REF][START_REF] Noiruksar | Application of Remote Sensing for Aboveground Carbon Storage Determination of Teak in Khun Mae Kham Mee Plantation, Prae province[END_REF][START_REF] Zheng | Combining remote sensing imagery and forest age inventory for biomass mapping[END_REF][START_REF] Arko | Uncertainties in segmentation and their visualisation application of geoinformatics[END_REF][START_REF] Picard | Manual for Building Tree Volume and Biomass Allometric Equations[END_REF][START_REF] Sedjo | Forest carbon sequestration: some issues for forest investment[END_REF][START_REF]United Nation Framework Convention on Climate Change, Kyoto Protocol[END_REF][START_REF]Production of Natural Rubber[END_REF][START_REF] Viriyabuncha | Handbook of Stand Biomass Estimation[END_REF][START_REF] Chantuma | Rubber new planting in Thailand: towards the world affected on climate change[END_REF][START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF][START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF][START_REF] Fuchs | Estimating aboveground carbon in a catchment of the Siberian forest tundra:combining satellite imagery and field inventory[END_REF][START_REF] Lu | Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin[END_REF][START_REF] Iglesias | Determination of carbon sequestration and storage capacity of eucalyptus plantation in Sra Kaew Province[END_REF][START_REF] Nilubol | Geo-Informatics Technology for Age Class Identification for Para rubber Plantation in Krabi Province[END_REF][START_REF] Noiruksar | Application of Remote Sensing for Aboveground Carbon Storage Determination of Teak in Khun Mae Kham Mee Plantation, Prae province[END_REF][START_REF] Zheng | Combining remote sensing imagery and forest age inventory for biomass mapping[END_REF][START_REF] Arko | Uncertainties in segmentation and their visualisation application of geoinformatics[END_REF][START_REF] Picard | Manual for Building Tree Volume and Biomass Allometric Equations[END_REF][START_REF] Mongkolsawat | An approach for Estimating Area of Rubber Plantation: Integrating Satellite and Physical Data Over the North East Thailand[END_REF].

{ | = ; ∑ f -∑ f F * f F! } } P! ; -∑ f F! * f F! } P! (3-18)
where:

K ˆ = Points

CHAPTER IV

RESULTS AND DISCUSSION

The results following by: field data, ground biomass and carbon stock estimation are first explored. Secondly, the THAICHOTE image is calibrated. Then, the image classification, Para rubber surface extraction and biomass and carbon estimation are described. Finally, the consequences of the results of biomass and carbon estimation over the Mae num Prasae watershed are explored. Therefore, the results of this study are discussed.

Field data

A total sample 500 plots of Para rubber plantation were collected in the study area

Ground Biomass and Carbon Stock Estimation

The estimation of a single tree stand biomass and carbon stock was used the allometric

Image Classification

An 17 layers were computed in the image classification information of this study. Four single bands and five vegetation indexes were integrated in the spectral information. The vegetation indexes were computed using map algebra in Red and NIR bands ration. The spectral information was listed in Table 4-5 and sample layers were shown in Figure 4-10 to 4-12. The 8 texture indexes of textural information were computed using GLCM method.

The textural information was listed in Table 4-5 and sample layers were shown in Figure 4 Additionally, the GIS database obtained by NSDI (Thai National Spatial Data Infrastructure) was evaluated to the mask information. The areas not concerned by the study were masked using urban areas, natural forest areas and other agricultural crops. Thus, the total surface of Para rubber plantation is shown to 154.34 km 2 .

Doctor of Philosophy -Kitsanai Charoenjit

Map of Para rubber plantation limit

Ten thousand and sixty nine Para rubber plantations were identified with a maximum surface of 0.23 km 2 and a minimum surface of 1,600 m 2 while, the mean surface was 15,329 m 2 . Figure 4-17 shows a sample of the map of Para rubber plantations obtained from the image segmentation process. The uncertainty of Para rubber plantation surface was calculated from comparison of multi scale segmentation and manual digitization (Figure 4-18). The total surface of plantation is 154.34 km 2 with an uncertainty of 11% (+/-17 km 2 ). The number of Para rubber tree stand was calculated using the constant value for 475 trees ha -1 obtained by field measurement. Thus, the total number of tree stand is approximately of 7,321,444 trees (+/-802,832 trees). where Y is the tree girth (m) and R 2 is 0.87., Scatter plot of TGM#1 and TGM#2 is shown in This equation was applied of the layers defining each plantation in order to estimate the girth of the trees of each plantation. The model (TGM#2) was realized in the GIS built for this study. From the tree girth map, a map of tree ages has been drawn (Figure 4

Tree girth model (TGM) and Para rubber tree Classification

Estimation of Biomass and Carbon Stock

The accuracy of the model TGM#2 was evaluated using the root mean square error (RMSE) and the mean absolute percentage (MAPE). The RMSE and MAPE are given to 0.17 tC ha -1 and 8.33% respectively. The errors from the surface and the model were summarized.

Consequently, the total uncertainty of the biomass and carbon stock estimation is given to

Result's Discussion

The objective of this study was to connect biomass and carbon inventory data to The forest CHM (canopy height model) obtained by the photogrammetric techniques is very complex, high variation in reflectance anisotropy, and the matching accuracy for such a surface may be flawed by:

(1) little or no texture (corresponding to photographic quality);

(2) moving objects (such as leaves, tree apex and shadows);

(3) occlusions;

(4) radiometric artifacts

(5) high angle or wide baseline [59 -62].

Nevertheless, the CHM quality is not editing by hand and beyond in the scope of this studied.

The accuracy CHM was tested using the mean absolute percentage error (MAPE) and shown up to 70% thus the CHM obtained by THAICHOTE stereo data is unsuited for biomass forest evaluation.

The Para rubber plantation surface extraction is depended on the NSDI (Thai National Spatial Data Infrastructure) data for establishing the mask information that the Para rubber may increasing and decreasing for a surface calculation. To minimizing the uncertainty data, the good quality and lasted NSDI data are needed to build the mask information.

The Para rubber plantation is non-evergreen forest type. In study area, the leaves of The Para rubber plantation is a one candidate to stocking carbon, the result of this estimation shown the high potentiality carbon stocked of 112.5 -228.58 tC ha -1 at mature stage (rotation stage) when comparing for the other plantations in Thailand [START_REF] Iglesias | Determination of carbon sequestration and storage capacity of eucalyptus plantation in Sra Kaew Province[END_REF][START_REF] Noiruksar | Application of Remote Sensing for Aboveground Carbon Storage Determination of Teak in Khun Mae Kham Mee Plantation, Prae province[END_REF]. • Place the reference tape at the tree,

• Determine an optimal distance to the tree by checking the view field to tree bottom and top within the forest stand -preferably 15, 20, 25 or 30m.

Height measurement

• Select the corresponding height scale by turning the adjustment disk,

• Sight tree bottom, lock the pointer needle and remember the value,

• Sight tree crown, lock the pointer needle and remember the value,

• The difference between the measurements will be tree height : htree = hcanopy -hbottom

2 2 - 1 3 - 5 formula 4 - 1 4 - 4 3 4- 5 4 - 6

 2213541443546 Field measurement: Data sheet for collecting forest inventory data A.3 The input dataset for tree girth modeling (TGM) and Histogram A.4 Articles publication ix Para rubber productivity of the world in 2011 2-2 Performed percentage carbon and elements on parts of RRIM 600 2-3 (a) THAICHOTE performance., (b) THAICHOTE payload optical instruments characteristics 2-4 Textural information equations 3-1 Data sources 3-2 Gain and Bias in each band of THAICHOTE data used in the study 3-3 The parameters input to the model 3-4 Distance from Earth to the Sun (Astronomical Unit) Textural Field data collected from December 2011 to April 2012: view from ground and view from THAICHOTE satellite data (band composite RGB:NIR, Red, Green) for the Para rubber ages used in this study 4-2 The rate capacity of carbon sequestration, biomass and carbon stock obtained by single tree stand of Para rubber tree 4-3 Statistical of THAICHOTE satellite data. It was corrected radiometry using COST model (reflectance data) The rate capacity of carbon sequestration, biomass and carbon stock obtained by individual tree stand of Para rubber tree., Veg.1-3 is vegetation1-Statistical of image classification layers obtained by spectral and textural information Statistical of image characteristics measured at the emplacement where field data were acquired association to the girth and age of trees of each plantation… x

4- 7 2 2 - 8 "

 7228 Figure Page 1-1 (a).THAICHOTE false-color image (RGB: NIR,Red,Blue, spatial resolution of 2 m., (b). Rubber age class map derived from pixel based classification with coverage by salt-pepper noise and misclassified some age class of rubber plantation., (c). Rubber age class map derived from object based classification. 1-2 State of the art of forest biomass estimation on large-surface 1-3 The Mae num Prasae watershed 1-4 Conceptual Framework 1-5 Structure of Thesis 2-1 Para rubber plantation (RRIM 600) 2-2 Diagram showing the processes and fluxes maintaining or modifying the different C stocks in a forest, and the fluxes of the other key greenhouse gases, CH4 and N2O. Green arrows indicate C flux into the forest and between various C stocks; red arrows indicate C fluxes out of the forest 2-3 Organization of a biomass measurement… 2-4 Relationship between girth (cm) at 1.7 meter above ground and dry weight biomass per tree RRIM 600 age 2 -25 years… 2-5 (a) THAICHOTE performance., (b) Spectral signature of THAICHOTE 2-6 Example illustration of the probability matrix generation… 2-7 Top-down view of a simple stereo system highlighting significance of correspondence problem 2-8 "Normal Case" configuration for calculation of object space coordinates 2-9 Object-oriented image analysis: the generic procedure 3-1 Study process 3-2 The equipment of forest field measurements…

3- 3 4 - 2 4 - 8

 34248 Sample plot measurements… 3-4 The processes of Image Classification 3-5 The processes of textural information 3-6 The processes of 3D information 3-7 The processes of Para rubber surface extraction 4-1 The Mae num Prasae watershed on THAICHOTE satellite data (yellow boundary) The relationship between tree age and girth (R 2 = 0.96) 4-3 Planting layout of Para rubber plantation in study area 4-4 The biomass and carbon stocked in single tree of Para rubber 4-5 THAICHOTE satellite natural color (R, G, B) image corrected 4-6 THAICHOTE satellite false color (NIR, G, B) image corrected. 4-7 The sample reflectance data in the study area obtained by THAICHOTE image The spectral signature obtained by THAICHOTE satellite image noncorrected according to Water vegetation and bare soil object 4-9 The spectral signature obtained by THAICHOTE satellite image corrected according to Water vegetation and bare soil object 4-10 Example layers of Spectral information (Single bands)… 4-11 Example layers of Spectral information (Vegetation indexes)., (a) ARVI., (b) GEMI., (c) IRVI., (d) MSAVI2… 4-12 Example layers of Spectral information (Vegetation indexes)., (e) NDVI., (z) THAICHOTE false color image (NIR, G, B).

4- 13

 13 Example layers of Textural information (Texture measures)., (a) CON., (b) DIS., (c) HOMO., (d) ASM., (z) THAICHOTE false color image (NIR, G, B). 4-14 Example layers of Textural information (Texture measures)., (e) ENT., (f) Mean., (g) Var., (h) Cor., (z) THAICHOTE false color image (NIR, G, B). 4-15 Example images of THAICHOTE stereo pair data segmentation 4-16 Example layers of 3D information… 4-17 Example map of Para rubber plantations extracted from the multi-scale segmentation. Yellow polygon is Para rubber plantation limits 4-18 Example map of Para rubber plantations limit extracted from the multiscale segmentation (Yellow polygon) and the manual digitization (Green polygon) 4-19 The graphics obtained from mean, skewness and kurtosis of the image classification layers of each age class of Para rubber plantation. 4-20 Pearson's correlation coefficient graph 4-21 (a) TGM#1 derived from a spectral information, (b) TGM#2 derived from a combination of spectral and textural information 4-22 Map of Para rubber age class 4-23 (a) Bar graphs: biomass, carbon stock and uncertainties data. (b) The rate of CO2 sequestration by Para rubber of each age class 4-24 Map of Para rubber carbon stock. 4-25 (a) Para rubber plantation limits (segmentation)., (b) Bar graph: textural information and age class mapxiv

Figure 1 - 1 shows

 11 "salt-pepper noise" on Para rubber plantation mapping. THAICHOTE false-color imagery (Figure 1-1(a) ) was classified by classical pixel-based technique (Figure 1-1(b) ).

Figure 1 - 1

 11 Figure 1-1 (a). THAICHOTE false-color image (RGB: NIR, Red, Blue, spatial resolution of 2 m., (b). Rubber age class map derived from pixel based classification with coverage by salt-pepper noise and misclassified some age class of rubber plantation., (c). Rubber age class map derived from object based classification.

Figure 1 - 2

 12 Figure 1-2 State of the art of forest biomass estimation on large-surface.

9

 9 Object-based classification[START_REF] Guo | An Object-Based Classification Approach in Mapping Tree Mortality Using High Spatial Resolution Imagery[END_REF] [START_REF] Guo | An Object-Based Classification Approach in Mapping Tree Mortality Using High Spatial Resolution Imagery[END_REF] studied the Object-Based Classification Approach in Mapping Tree Mortality Using High Spatial Resolution Imagery in California. They developed an object-based approach, including an image segmentation process and a knowledge-based classifier, to detect individual tree mortality in imagery of 1 m spatial resolution. The combined segmentation and classification methods provided an easy and intuitive way to incorporate human knowledge into the classification process. The results shown that, the object-based approach significantly outperformed a pixel-based maximum likelihood classification method in mapping the tree mortality on high-spatial-resolution multispectral imagery.[START_REF] Kim | Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery: Segmentation Quality and Image Classification Issues[END_REF] [START_REF] Kim | Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery: Segmentation Quality and Image Classification Issues[END_REF] studied the use of a geographic object-based image analysis (GEOBIA) approach with the incorporation of object-specific grey-level co-occurrence matrix (GLCM) texture measures in Guilford Courthouse National Military Park, North Carolina.Imagery was obtained from a multispectral IKONOS image for delineation of deciduous, evergreen, and mixed forest types in the studied. A series of automated segmentations wasDoctor of Philosophy -Kitsanai CharoenjitIntroduction/Estimation of Biomass and Carbon Stock in Para rubber plantation in East ThailandUsing Object-based Classification from THAICHOTE Satellite Data produced at a range of scales, each resulting in an associated range of number and size of objects (or segments). They found, an overall accuracy of 79 percent with a Kappa of 0.65 was obtained at the optimal segmentation scale of 19. The addition of object-specific GLCM multiple texture analysis improved classification accuracies up to a value of 83 percent overall accuracy and a Kappa of 0.71 by reducing the confusion between evergreen and mixed forest types. Some misclassification still remained because of local segmentation quality, a visual assessment of the texture-enhanced GEOBIA classification generally agreeable with manually interpreted forest types.

Introduction/ 10

 10 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data measuring of Para rubber biomass and carbon stock can be done using the object based classification H1: The object based classification can be applied and minimized uncertainty data for estimating the Para rubber biomass and carbon stock.

Figure 1 - 3

 13 Figure 1-3 The Mae num Prasae watershed (georeferenced on UTM WGS 1984 Z48N).

Figure 1 - 4 .

 14 Figure 1-4. shows the concept of the carbon dioxide elimination using the forest plantation. The carbon can be stored in the Para rubber tree, capturing by the leaves thru photosynthesis process with the aid of light and water these are processed and stored in the stems of trees as biomass.

Figure 1 - 4

 14 Figure 1-4 Conceptual Framework.

Figure 2 - 1

 21 Figure 2-1 Para rubber plantation (RRIM 600).

Figure 2

 2 Figure 2-2.

Figure 2 - 2

 22 Figure 2-2 Diagram showing the processes and fluxes maintaining or modifying the different C stocks in a forest, and the fluxes of the other key greenhouse gases, CH4 and N2O. Green arrows indicate C flux into the forest and between various C stocks; red arrows indicate C fluxes out of the forest. [25]

Figure 2 - 3

 23 Figure 2-3 Organization of a biomass measurement site with 7 different operations.Operation 1, site preparation and felling of the trees, operation 2, measurement of felled trees: stem profile, marking for cross-cutting, operation 3, stripping of leaves and limbing, operation 4, cross-cutting into logs and disks, operation 5, weighing of logs and brushwood, operation 6, sampling of branches, operation 7, sample weighing area.[START_REF] Kira | Primary production and turnover of organic matter in different ecosystems of the Western Pacific[END_REF]

Figure 2 - 4

 24 Figure 2-4 Relationship between girth (cm) at 1.7 meter above ground and dry weight biomass per tree RRIM 600 age 2 -25 years, number of samples equals 95 trees. [7]

3 and

 3 THAICHOTE (previously name THEOS), THEOS is an Earth observation mission ofThailand, under development at EADS Astrium SAS, Toulouse, France. In July 2004, EADS Astrium SAS signed a contract for delivery of THEOS with GISTDA (Geo-Informatics and Space Technology Development Agency) of Bangkok, Thailand. GISTDA is Thailand's leading national organization (i.e., space agency) in the field of space activities and applications. The Thai Ministry of Science and Technology is funding the program.The THEOS cooperative agreement includes the production and launch of one optical imaging satellite, as well as the development of the ground segment necessary to operate and control the satellite directly from Thailand. The contract also specifies on-the-job training of Thai engineers as part of the EADS Astrium development team. Also as part of the THEOS program, GISTDA earned the right to receive data from the SPOT-2, 4 and 5 spacecraft of CNES in Thailand, which have many features similar to those of THEOS[START_REF] Kaewmanee | Thailand Earth Observation System: Mission and Products[END_REF].The prime objective of THEOS is to provide Thailand with an affordable access to space (i.e., a state-of-the-art Earth observation satellite for the near future), and to spawn through this program's operational experience the country's own capability and infrastructure an indigenous potential for the development of future space missions.The science objectives call for the provision of: • Panchromatic (2 m) and multispectral (15 m) imagery from THEOS observations, and • The generation of geo-referenced image products and image processing capabilities for applications in the fields of cartography, land use, agricultural monitoring, forestry management, coastal zone monitoring and flood risk management. Doctor of Philosophy -Kitsanai Charoenjit Literature Review/ 24 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data The THAICHOTE performance and payload characteristics are shown in Table 2-Figure 2-5.

Figure 2 - 5

 25 Figure 2-5 (a) Spectral signature of THAICHOTE., (b) THAICHOTE performance.

  Broadband greenness vegetation indexes (VIs) are among the simplest measures of the general quantity and vigor of green vegetation. They are combinations of reflectance measurements that are sensitive to the combined effects of foliage chlorophyll concentration, canopy leaf area, foliage clumping, and canopy architecture. These VIs are designed to provide a measure of the overall amount and quality of photosynthetic material in vegetation, which is essential for understanding the state of vegetation for any purpose.Broadband greenness VIs compare reflectance measurements from the reflectance peak of vegetation in the near-infrared range to another measurement taken in the red range, where chlorophyll absorbs photons to store into energy through photosynthesis. Use of nearinfrared (NIR) measurements, with much greater penetration depth through the canopy than red, allows sounding of the total amount of green vegetation in the column until the signal saturates at very high levels. Because these features are spectrally quite broad, many of the broadband greenness indices can work effectively, even with image data collected from broadband multispectral sensors, such as AVHRR, Landsat TM, THAICHOTE, QUICKBIRD. Applications include vegetation phenology (growth) studies, land-use and climatological impact assessments, and vegetation productivity modeling[START_REF]Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data[END_REF]. Basic example of broadband greenness Vis indexes obtained by NIR and Red ratio from THAICHOTE data are listed in below.Doctor of Philosophy -Kitsanai CharoenjitLiterature Review/ 27 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data • Normalized Difference Vegetation Index (NDVI)The Normalized Difference Vegetation Index: NDVI[START_REF] Rouse | Monitoring vegetation systems in the great plains with ERTS[END_REF] is image enhancing the green vegetation obtained by transformation of the visible (red) and near-infrared bands of satellite information. The NDVI values range from -1 to 1. Healthy vegetation will have a high NDVI value. Bare soil and rock will have NDVI values near zero. Clouds, water, and snow are negative NDVI values. The NDVI is calculated with the following Equation (2-1).

Figure 2 - 6

 26 Figure 2-6 Example illustration of the probability matrix generation, considering east direction only (1 pixel offset from a reference pixel and its immediate neighbor) and summary

  The matrix framework for the east(1,0) spatial relationship Probability matrix (co-occurrence matrix ) Probability calculate step by step Sum of total elements = 12 eg. (0,0) 2/12 = 0.167 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

  Measure of the order in the image. It is related to the energy required for arranging the elements in the system.Entropy (ENT)

Figure 2 - 7 F 6 )

 276 Figure 2-7 Top-down view of a simple stereo system highlighting significance of correspondence problem.[START_REF]The Exelis Visual Information Solutions product documentation center[END_REF]

  the object in the left image x2 = x position of the object in the right image pd = estimated principle distance B = stereo base separation Doctor of Philosophy -Kitsanai Charoenjit Literature Review/ 34 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Figure 2 - 8 "

 28 Figure 2-8 "Normal Case" configuration for calculation of object space coordinates.[START_REF]The Exelis Visual Information Solutions product documentation center[END_REF]

Figure 2 - 9 37 Figure 3 - 1 .

 293731 Figure 2-9Object-oriented image analysis: the generic procedure.[START_REF]Object-Based Image Analysis Spatial Concepts for Knowledge-Driven Remote Sensing Applications Series[END_REF]

Figure 3 - 1

 31 Figure 3-1 Study process.

  -3(a)). Random plots were used for the forest inventory associated to tree age range from 4 to 25 years old. Every plot was collected geo-location using GPS handheld measured at center point of the plot. The study also measured the tree stand density. The age data was classified in 8 classes of 4, 8, 12, 16, 18, 20, 22 and 25 years old. DBH and tree girth were measured with a diameter tape at height 1.3 to 1.7 m above the ground according to the position of latex tapped skin on the trunk (Figure 3-3(b), 3-3(c)). Data was managed in a GIS database which included topographic map and satellite image projected in the Universal Transverse Mercator (UTM) 48N Zone on World Geodetic System 1984 (WGS84). Doctor of Philosophy -Kitsanai Charoenjit Data & Methods/ 40 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Figure 3 - 2 Figure 3 - 3 43 3 . 3 . 1

 323343331 Figure 3-2 The equipment of forest field measurements. (a) a field computer notebook, (b) an altimeter (measuring tree height), (c) a diameter tape, (d) a field tape, (e) a GPS and (f) a Camera GPS.

  in the Universal Transverse Mercator projection Zone 48 North on Word Geodetic System 1984 ellipsoid (UTM WGS-1984 Z48N) and corrected from topographic distortion using ASTER Global Digital Elevation (GDEM Version 2). GDEM was revised spatial resolution from 30 m to 2 m using raster resample technique. Therefore, THAICHOTE image has been provided in 2 m spatial resolution and 4 single bands of Blue (0.45-0.52 µm), Green (0.53-0.60 µm), Red (0.62-0.69 µm) and Near Infrared (NIR, 0.77-0.90 µm). The cosine of the solar zenith corrections (COST) [48] was used to correct the radiometry of the image. The sun azimuth was 143.21° and the sun elevation was 44.5710°. The inputs to the model are the Earth-Sun Distance, sun elevation angle, and digital number Doctor of Philosophy -Kitsanai Charoenjit Data & Methods/ 46 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data (DN) values for each band. The parameters input were shown in Table 3-2, 3-3 and 3-4. The image DN value was first transferred to spectral radiance value using Equation (3-4): the sensor's aperture (Mw*cm -2 *ster -1 *µm -1 ) DN = Digital number (8-bit, 0-255) Gain = Absolute calibration gain of each band (mW.cm -2 .µm -1 ) Bias = Absolute calibration bias of each band (mW.cm -2 .µm -1 )

Figure 3

 3 Figure 3-4):

Figure 3 - 4

 34 Figure 3-4 The processes of Image Classification.

49 Equation

 49 Estimation of Biomass and Carbon Stock in Para rubber plantation in East ThailandUsing Object-based Classification from THAICHOTE Satellite Data•Global Environment Monitoring Index (GEMI)[START_REF] Pinty | GEMI: a non-linear index to monitor global vegetation from satellites[END_REF] was determined using the

  value (spectral band 0.62 -0.69 µm) BLUE = Visible blue reflectance value (spectral band 0.53 -0.6 µm) Doctor of Philosophy -Kitsanai Charoenjit Data & Methods/ 50 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

52

 52 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data statistics , σi , σj = The mean and standard deviation of Pi,j respectively

  Model (CHM) and it was combined in the image classification layers. The CHM was estimated using the THAICHOTE stereo pair data. The Epipolar image, Digital Surface model (DSM) and Digital Terrain Model (DTM) were generated in this process. The 3D information generation is illustrated in Figure 3-6 and the steps of process are descripted as follows: Doctor of Philosophy -Kitsanai Charoenjit Data & Methods/ 53 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Figure 3 - 6

 36 Figure 3-6 The processes of 3D information.

Figure 3 - 7

 37 Figure 3-7 The processes of Para rubber surface extraction.

Data & Methods/ 56

 56 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data between precision and generalization. Therefore, the total number of tree was estimated by GIS area calculation from the tree density of plantations. The multi-scale segmentation shown in Equation (3-12): 8 2 = c + 2.

  g hi = Sum of the cross-products Doctor of Philosophy -Kitsanai Charoenjit Data & Methods/ 57 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data g hh , g ii = Sum of square of the X and Y values • Tree Girth Model (TGM) from spectral and textural indexes)

(3- 15 )

 15 Doctor of Philosophy -Kitsanai CharoenjitData & Methods/ 58 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

(

  from December 2011 to April 2012) (Figure 4-1). The relationship obtained from tree age and girth was shown to strongest than tree height with correlation R 2 of 0.95 and 0.86 respectively (Figure 4-2). The highest girth was shown on the tree age 25 years old (90 cm) while the lowest girth is shown on the tree age 4 years old (32 cm). The tree height was shown maximum to 26 m on age 25 years while shown minimum to 7 m on age 4 years. The Para rubber plantations in the study area were clustered to young age class (4 to 12 years old) was less than 53 cm while middle age class (12 to 18 years old) was 53 to 65 cm and old age class (> 18 years old) was more than 65 cm. The Para rubber plantation statistics show in Table 4-1. Cultivation of the Para rubber plantation has a traditional spatial distribution. Trees are spaced of 3 m on lines spaced of 7 m (Figure 4-3). Thus, the density of Para rubber plantation is averaged to around 475 trees.ha -1 . Doctor of Philosophy -Kitsanai Charoenjit Results & Discussion / 61 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Figure 4 - 1

 41 Figure 4-1 The Mae num Prasae watershed on THAICHOTE satellite data (yellow boundary). The field data shown in green dots with UTM WGS 1984 Zone 48.

Figure 4 - 2 Table 4 - 1

 4241 Figure 4-2 The relationships of tree age, girth and height.

Figure 4 - 3

 43 Figure 4-3 Planting layout of Para rubber plantation in study area.

Figure 4 - 4 Figure 4 -

 444 Figure 4-4 The biomass and carbon stocked in single tree of Para rubber.

Figure 4 - 5 ¯

 45 Figure 4-5 THAICHOTE satellite natural color (R, G, B) image corrected. The UTM WGS 1984 Zone 48 was referenced.

Figure 4 - 6

 46 Figure 4-6 THAICHOTE satellite false color (NIR, G, B) image corrected. The UTM WGS 1984 Zone 48 was referenced.

¯

  Doctor of Philosophy -Kitsanai CharoenjitResults & Discussion / 67 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Figure 4 - 7

 47 Figure 4-7 The sample reflectance data in the study area obtained by THAICHOTE image (a). non-corrected radiometry and (b). corrected radiometry.

Figure 4 - 8

 48 Figure 4-8 The spectral signature obtained by THAICHOTE satellite image non-corrected according to Water, Vegetation and Bare soil object.

Figure 4 - 9

 49 Figure 4-9 The spectral signature obtained by THAICHOTE satellite image corrected according to Water vegetation and bare soil object.

- 13 to 4 - 14 .

 13414 In 3D information, the digital surface model (DSM) and digital terrain model (DTM) were first computed using THAICHOTE stereo pair data. DSM was shown a maximum height surface of 220 m and DTM was shown a maximum height surface of 195 m. The canopy height model (CHM) was not appropriated to build the image classification information for estimating the Para rubber biomass and carbon stock. The mean absolute percentage error (MAPE) was shown to 74% when compare the tree height data from field. 3D information were shown in Figure 4-15 and 4-16.

Results & Discussion / 70

 70 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Figure 4 - 10

 410 Figure 4-10 Example layers of Spectral information (Single bands)., (a) Blue., (b) Green., (c) Red., (d) NIR., (z) THAICHOTE false color image (NIR, G, B).

Figure 4 - 11

 411 Figure 4-11 Example layers of Spectral information (Vegetation indexes)., (a) ARVI., (b) GEMI., (c) IRVI., (d) MSAVI2., (z) THAICHOTE false color image (NIR, G, B).

  Doctor of Philosophy -Kitsanai CharoenjitResults & Discussion / 72 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Figure 4 - 12

 412 Figure 4-12 Example layers of Spectral information (Vegetation indexes)., (e) NDVI., (z) THAICHOTE false color image (NIR, G, B).

Figure 4 - 13

 413 Figure 4-13 Example layers of Textural information (Texture measures)., (a) CON., (b) DIS., (c) HOMO., (d) ASM., (z) THAICHOTE false color image (NIR, G, B).

  Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Figure 4 - 14

 414 Figure 4-14 Example layers of Textural information (Texture measures)., (e) ENT., (f) Mean., (g) Var., (h) Cor., (z) THAICHOTE false color image (NIR, G, B).

Figure 4 - 15

 415 Figure 4-15 Example images of THAICHOTE stereo pair data., (a) Forward acquired (along -37.471443°,across 21.013978°)., (b) Backward acquired (along 35.521264°, across 21.101122°).

Figure 4 - 16

 416 Figure 4-16 Example layers of 3D information., (a) THAICHOTE false color image (NIR, G, B)., (b) Digital Surface Model (DSM)., (c) Digital Terrain Model (DTM), (d) Canopy Height Model (CHM).

Figure 4 - 17 m

 417 Figure 4-17 Example map of Para rubber plantations extracted from the multi-scale segmentation. Yellow polygon is Para rubber plantation limits.

Figure 4 - 18

 418 Figure 4-18 Example map of Para rubber plantations limit extracted from the multi-scale segmentation (Yellow polygon) and the manual digitization (Green polygon).

  Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data COR while minimum was shown in ASM (1.92). Old age class, maximum skew was shown in CON (1.3) while minimum was shown in IPVI (-1.73) and maximum kurtosis was shown in IPVI (12.66) or highest value of all while minimum was shown in ARVI (2.48). Figure 4-19 shown the graphics obtained from mean, skewness and kurtosis of the image classification layers of each age class of Para rubber plantation. Additionally, the graphics of frequency histogram are shown in appendix (Figure 3A-1).

Figure 4 -

 4 Figure 4-20 Pearson's correlation coefficient graph.

Figure 4

 4 Figure 4-21.

Figure 4 -

 4 Figure 4-21 (a) TGM#1 derived from a spectral information., (b) TGM#2 derived from a combination of spectral and textural information.

Figure 4 - 22

 422 Figure 4-22 Map of Para rubber age class.

Figure 4 -

 4 Figure 4-22 (cont.) (a) THAICHOTE false color image., (b) Map of Para rubber age class.

Figure 4 - 1 )

 41 Figure 4-23 (a) Bar graphs: biomass, carbon stock and uncertainties data., (b) The rate of C stocking., (c) The rate of CO2 sequestration by Para rubber of each age class.

Figure 4 - 24

 424 Figure 4-24 Map of Para rubber carbon stock.

Figure 4 -

 4 Figure 4-24 (contin.) (a) THAICHOTE false color image (b) Map of Para rubber carbon stock.

( 1 )( 3 )

 13 THAICHOTE satellite image and to develop a tree girth model (TGM) to improve biomass and carbon stocks estimation in Para rubber plantations in East Thailand. The accuracy of Para rubber biomass and carbon stock estimation at large-surface realized on many interacting factors that might be distributed by: Remote sensing techniques -Image quality (Spatial resolution, Camera sensor, Data acquisition) -Image classification techniques (Tree counting, Tree age and girth identification) (2) Plantation Management -Tree species (clone of Rubber plantation) -Planting layout (density of tree stand per area) -Planting location (environmental conditions eg.soil, climate) Statistical Analysis for connecting ground data and remote sensing data -Fitted Modeling (Tree sampling, Parameterization, Relationship, Empirically and Globally models, Multicollinearity data) -Allometric equation (Empirically and Globally models) -Carbon concentration (ratio) • Tree Girth Modeling and Spectral, Textural information The obtain relationships between tree girth and layers used for image classification show that texture parameters are better correlated than single bands and vegetation indices as shown by the Pearson's correlation coefficient values.

  Para rubber fall on between February and May. The method developed in this study consider green plantation that the GEMI, HOMO, DIS and VAR are shown to negative for the canopy age of tree while the CON is shown to positive (Figure4-25). Additional work remains to be Doctor of Philosophy -Kitsanai Charoenjit Results & Discussion / 97 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data done to test the potentialities of THAICHOTE data acquired in the period during which trees have no leaves.

Figure 4 -

 4 Figure 4-25 (a) Para rubber plantation limits (segmentation)., (b) Bar graph: textural information and age class map.

  camaldulensis), Teak (Tectona grandis) and Para rubber plantation RRIM 600 (Hevea brasiliensis). Moreover, the results of the study are confirmed on the carbon stocking in the same order magnitude (214 tC ha -1 ) from the work of[START_REF] Kongsager | The carbon sequestration potential of tree crop plantations[END_REF] [START_REF] Kongsager | The carbon sequestration potential of tree crop plantations[END_REF] for estimation of rate carbon stock in tree clone Hevea brasiliensis using allometric equations.

•

  amount of CO2 captured by Para rubber plantations can be compared to natural carbon sequestration estimated for the ocean. Borges et al., (2005) [64] show that the sequestration of CO2 by ocean is around 4.45 gC m -2 yr -1 , while Para rubber plantation can sequester up to 914.3 gC m -2 yr -1 . The study believe that agriculture and human intervention may play a critical role in the extraction of CO2 from the atmosphere and thus in the short carbon cycle. Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data This study explored the potential of THAICHOTE satellite data to estimate Para rubber biomass and carbon stock. The method developed here used the object based classification combining the spectral, textural and 3D information on THAICHOTE satellite data. Two hypothesises of the study are accepted by H1. The textural information can be improved the relationship of Para rubber plantation biomass between field and remote sensing data and the precision measuring of Para rubber biomass and carbon stock can be done using the object based classification. The conclusions of the study are listed in below. The correlation between tree girth and values of the different layers obtained from the remote sensing image was tested by Pearson's correlation. The texture parameters were shown high correlation of absolute 0.75 to 0.88, excluded the texture mean that it shown to absolute 0.5. The broadband greenness vegetation indexes were shown medium correlation of absolute 0.51 to 0.66. The single bands were shown poor correlation (absolute value 0.01 to 0.21), excluded the NIR that it shown to absolute 0.68. The canopy height model (CHM) is not appropriated to build the image classification layers and it shown to high error (MAPE = 74%) for estimating the Para rubber biomass and carbon stock. The results of this study show that these data can be used to map Para rubber plantation and distinguish age classes of trees in the plantations. The study propose that textural information is more useful than spectral information to capture tree canopy architecture and thus the age of the canopy. Moreover, it has been possible to build a empirical model equation relating some textural parameters to the age of the plantation. In the Doctor of Philosophy -Kitsanai Charoenjit Conclusions & Perspectives/ 100 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data study, the tree girth model (TGM#2) obtained from multiple linear regression analysis (stepwise method) was shown correlation coefficient of R 2 =0.87 with accepted five layers (excluded twelve layers) and thus can be used with confidence on the study area. The TGM is written in formula: Y = 3.694 -1.29(GEMI) -2.740(HOMO) -0.933(DIS) + 0.068(CON) -0.015(VAR) where Y is Tree girth (m), From spectral information: GEMI is Global Environment Monitoring Index, Form textural information: HOMO is Homogeneity, DIS is Dissimilarity, CON is Contrast and VAR is Variance. Around 154 km 2 of the 232 km 2 of the studied area are covered by Para rubber plantations. The class of age of each plantation has been estimated to be 33% of the crop surface belong to the young class (from 4 to 12 years), 34% of the crop surface belong to the middle class (from 12 to 18 years) and 33% belong to the old class (older than 18 years).The rate of biomass stock, the age for four years old is lowest stocked biomass (11 tC ha -1 ) while the age for twenty-five years is highest stocked biomass (229 tC ha -1 ). The average value for the rate of biomass stock in the study area is 64 tC ha -1 . The way in which the rate increasing of biomass stock varied as a function of the girth and age of tree.The total amount of biomass and carbon stocks are 2.23 Megatons and 0.99 Megatons C respectively with uncertainty of 11%. In 2011, the total area sequestered 121 tCO2 by Para rubber plantations. Moreover, the results of this studied show that the Para rubber plantation (914.3 gC m -2 yr -1 ) has higher potential CO2 sequestration than the Ocean (4.45 gC m -2 yr -1 ) for around 200 time.Doctor of Philosophy -Kitsanai CharoenjitConclusions & Perspectives/ 101 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data5.2 PerspectivesDespite, the THAICHOTE satellite data, the object based classification and the tree girth model developed here are appropriated for estimation biomass and carbon stock in Para rubber plantation, the problems and the limitations of the study are needed to improve for minimizing the uncertainty data of forest biomass-carbon mapping. The study recommended for further research as following areas: Use of this study method in other Para rubber plantation areas could provide basis of comparison in terms of accuracy.• Consideration of the ground measuring CO2 flux in the Para rubber plantation by Eddy Covariance (EC) method, multi-spectral/hyper-spectral imageries and Light Detection and Ranging (LiDAR) from Unmanned Aerial System (UAS) for high precision of biomass and carbon stock and source estimation at large-surface.

Figure 1A- 1

 1 Figure 1A-1 Tree height measurement., (a) distance measurement using field tape., (b) height measurement using Haga altimeter.

Figure 1A- 2

 2 Figure 1A-2 Tree height measurement., (a) live view measuring tree bottom., (b) live view measuring tree canopy.

  [START_REF] Benzie | Canopy Height Modeling for Improved Forest Biomass Inventory[END_REF] [START_REF] Benzie | Canopy Height Modeling for Improved Forest Biomass Inventory[END_REF] studied the Canopy Height Modeling for Improved Forest Biomass Inventory in Obtario Canada. Using LiDAR and Semi Global Matching (SGM) on stereo photogarmetry method. The study found, height values modeled by LiDAR and SGM were strong correlated. The study recommended to incorporate the canopy height modeled and forest field-based to examination of forest biomass volume.[START_REF] Zhang | Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data[END_REF] [START_REF] Zhang | Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data[END_REF], studied the Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data. They present

		Forest biomass estimation in
		Large-scale Analysis
	Ground forest biomass estimation	Remote sensing data
		Remote sensing data
		Simple regression
	Tree destructive method	modeling
		Very High Resolution Image
	Tree allometric equation
		Pixel based classification
		-Spectral information
		-Textural information

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data • Canopy height information A. a new remote sensing based approach for estimating live forest aboveground biomass (AGB) based on a simple parametric model that combines high-resolution estimates of leaf area index (LAI) from the Landsat Thematic Mapper sensor and canopy maximum height from the Geoscience Laser Altimeter System (GLAS) sensor onboard ICESat, the Ice, Cloud, and land

1.9 Structure of Thesis Chapter I Introduction: introduce

	Doctor of Philosophy -Kitsanai Charoenjit	Introduction/ 13
	Chapter II Literature Review: introduce to the general information of Para rubber
	plantation, Forest Carbon cycle and Ground forest biomass inventory. Then the general
	information of remote sensing used in this study are descripted associated to Remote sensing
	for vegetation indices, texture measurement, 3D terrain model and Object based classification.
	Chapter III Data and Methods: THAICHOTE satellite data and GIS database are
	included to the material setting. The method defined into 4 board parts. Forest inventory and
	Ground Biomass and Carbon estimation are combined in Part 1. The image processing is
	employed using 3 steps of Satellite Image Preprocessing (Part 2), Image Classification (Part
	3) and Para rubber Surface Extraction (Part 4). Finally, the total biomass and carbon stock at
	large surface are estimated.
	Chapter IV Results and Discussion: the results are explained in the following area:
	Field data, Ground Biomass and Carbon Stock Estimation, THAICHOTE image corrected,
	Image classification, Relationship between field data and remote sensing data, Para rubber
	surface extraction and Biomass and Carbon Stock Estimation over the Mae num Prasae
	watershed. Finally, the results of the study is discussed. The discussion are the limitation of
	the study method for estimate biomass and carbon stock such as the best parametric model for
	linking the field data and remote sensing data, comparison of this resulted and other studied,
	uncertainty data of biomass and carbon estimation and THAICHOTE image.
	to global warming and forest as a tool for playing
	on clean development mechanism, problematic on remote sensing classification for biomass
	and carbon stocks estimation. The state-of-the-art of forest biomass estimation at large-
	surface and the related works are first descripted and then Objectives of the study, Study
	Limitation are identified.
	Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand
	Using Object-based Classification from THAICHOTE Satellite Data

Chapter V Conclusions and Perspectives: Conclusions explained the parameterization

	severe dry season and a 125-150 raining days per year. The minimum and maximum
	temperature should be 20°C and 35°C respectively and atmospheric humidity should be 80-90
	for biomass estimation, the correlation of spectral and textural information from
	THAICHOTE satellite data, total amount of the surface of Para rubber age class, total
	capacity of biomass and carbon stock and the rate of CO2 sequestration by Para rubber
	plantation and further work.
	The thesis structure is shown in Figure 1-4.
	Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand
	Using Object-based Classification from THAICHOTE Satellite Data

Table 2 -1

 2 Para rubber productivity of the world in 2011.

	Country	Product average (1,000 ton)
	Thailand	3,527.30
	Indonesia	3,093.00
	Malaysia	996.3
	India	892.7

  Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data girth (cm) at 1.7 m above ground. This equation was developed by measurements realized on plots located in North, North East, South and East of Thailand.

	Doctor of Philosophy -Kitsanai Charoenjit	Literature Review/ 21
	2.5623 , a coefficient of determination R 2 = 0.9617, Y is the tree dry biomass (kg), X is

Table 2 -2 Performed

 2

	Part of Para rubber tree (RRIM 600)	% C
	Wood 0-3 meter above ground	44.5
	Bark 0-3 meter above ground	41.02
	Wood 9-12 meter above ground	45.3
	Bark 9-12 meter above ground	42.5
	Wood lateral root	45.88
	Bark lateral root	41.51
	Wood tap root	44.39

percentage carbon and elements on parts of RRIM 600.

[START_REF] Chantuma | Rubber new planting in Thailand: towards the world affected on climate change[END_REF]

Literature Review/ 22

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 2 -

 2 3 (a) THAICHOTE performance., (b) THAICHOTE payload optical instruments

	characteristics.
	(a)
	Orbit Type		Circular SSO low earth orbit
	Attitude		822 km
	Number of Orbits per Day		14+5 / 26 orbits per day
	Local Equator Crossing Time		10.00 am (descending)
	Orbit Period		101 minutes
	Orbit inclination		98.7
	Orbit Cycle		26 days (369 orbits)
			108 km (between 2 closest passes),
	Distance Between Passes (at Equator)	2,800 km (between 2 consecutive passes)
	Coverage Area		Radius more than 2000 km from Ground Station
	(b)
	Parameter	Panchromatic (Pan) Camera Multispectral (MS) Camera
			B1 (blue): 0.45-0.52 µm
			B2 (green): 0.53-060 µm
	Spectral range		0.45 to 0.90 µm
			B3 (red): 0.62-0.69 µm
			B4 (NIR): 0.77-0.90 µm
	Spatial resolution		2 m GSD	15 m GSD
	Swath width		22 km (nadir)	90 km (nadir)
	Image dynamics		8 bits among 12 bits	8 bits among 12 bits

Source: Geo-Informatics and Space Technology Development Agency (GISTDA).

Doctor of Philosophy -Kitsanai Charoenjit

Literature Review/ 25 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 2 - 4

 24 Textural information equations.[START_REF] Haralick | Textural features for image classification[END_REF]

	Texture group	GLCM descriptor	Formula	Description
		Contrast (CON)
	Contrast

  Image matching finds the conjugate points on both the left and right images which correspond to the same ground feature. The output of the image matching procedure is called a parallax image, in which the x-coordinate difference

• Epipolar Image Creation: Creating epipolar images is an essential processing step in Digital Elevation Model (DEM) extraction. Epipolar geometry describes the geometrical constraint between two frame images of a stereo pair. It represents the fact that a ground point and the two optical centers lie on the same plane. This means that for a given point in one image, its conjugate point in the other image must lie on a known line in the second image. By creating epipolar images, the search space for finding corresponding image points in automatic image matching is reduced.

Doctor of Philosophy -Kitsanai Charoenjit

Literature Review/ 35 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data • Image Matching:

Table 3 -

 3 1.

	Doctor of Philosophy -Kitsanai Charoenjit

Data & Methods/ 44 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 3 - 1

 31 Data sources. Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

		Category data 1 : Multispectral data (spatial resolution 15 m.)
	Scene ID		Center of	Bands	Cloud Acquisition	Sources
				position		date
	SCENE T1 M		E101°31' 36"	1 (0.45-0.52 µm.)	<10% December	GISTDA.
	2011/12/27			N12°53' 26"	2 (0.53-0.60 µm.)	27, 2011
	03:22:18.0 0265-			3 (0.62-0.69 µm.) 4 (0.77-0.90 µm.)
	0323
	3700
				Panchromatic data (spatial resolution 2 m.)
	SCENE T1 P		E101°31' 44"	0.45-0.90 µm.	<10% December	GISTDA.
	2011/12/27			N12°53' 46"		27, 2011
	03:22:17.9 0265-
	0324 5000
	SCENE T1 P		E101°28' 59"	0.45-0.90 µm.	<10% December	GISTDA.
	2011/12/27			N12°41' 45"		27, 2011
	03:22:21.3 0265-
	0324 5000
					Stereo data
	Scene ID		Center of	Viewing angle	Cloud Acquisition	Sources
				position	(degree)	date
	SCENE	T1	P	E101°32' 34"	along	<20% April	GISTDA.
	2012/04/03			N12°54' 06"	-37.465028°	3,2012
	03:37:41.4	0265-		across
	0324 0				21.010437°
	SCENE	T1	P	E101°35' 32"	along	<20% April	GISTDA.
	2012/04/03			N13°06' 05"	-37.471443°	3,2012
	03:37:38.0	0265-		across
	0324 0				21.013978°
	SCENE T1 P		E101°32' 44"	along	<20% April	GISTDA.
	2012/04/03			N12°54' 53"	35.524439°	3,2012
	03:34:28.3 0265-			across
	0324 1200				21.101462°
	SCENE T1 P		E101°35' 38"	along	<20% April	GISTDA.
	2012/04/03			N13°06' 55"	35.521264°	3,2012
	03:34:24.9 0265-			across

data 2 : Thailand topographic map scale 1:50,000 (series L7018)

		Map index		Sources
	5235II, 5335III, 5234I, 5334IV, 5234II, 5334III	Royal Thai Survey Department
		Digital Elevation Model (DEM)
	Product type	Spatial		Sources
		Resolution
	DEM	30	Global Digital Elevation Model (GDEM V.2)
		Category data 3 : GIS database from NSDI
	Product type	Scale		Sources
	Forest	1:50,000	Forest Department, Thailand.
	Land use	1:4,000	Land Development Department, Thailand.
	Road	1:50,000	Department of Highways, Thailand,
	Soil series	1:250,000	Land Development Department, Thailand.
	Admin boundary 1:50,000	Department of Provincial Administration, Thailand.

Table 3 - 2

 32 Gain and Bias in each band of THAICHOTE data used in the study.

		Band		Gain	Bias
	Blue			3.42038	0
	Green			2.12232	0
	Red			2.07681	0
	NIR			1.67119	0
	Then, the spectral radiance value was applied to COST model (assumed to have a
	reflectance of 1% or called LλHaze) that following Equation (3-5):
			1 2 =	3. 4 25 4 26 7 . 89:; 2 . <9=	(3-5)
	where:
	ρλ	=	Planetary reflectance (unitless)
	π	=	Mathematical constant approximately equal to 3.14159
	Lλ	=	Spectral radiance at the sensor's aperture (mW.cm -2 .µm -1 )
	LλHaze =	A haze correction (Lλ -Lλ1%) as in Table 3-3

d

= Distance from earth to sun (Astronomical Unit) as in Table 3-4 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 3 - 3

 33 The parameters input to the model.

	Band	Lλ1%	ESUNλ
	Blue	-2.952287	2005.824
	Green	-2.497447	1848.653
	Red	-2.039939	1566.489
	NIR	-1.126369	1064.725

Source: Geo-Informatics and Space Technology Development Agency (GISTDA).

Table 3 -

 3

[START_REF]United Nation Framework Convention on Climate Change, Kyoto Protocol[END_REF]

Distance from Earth to the Sun (Astronomical Unit).

  Entropy) and the descriptive statistics group (Mean, Variance, and Correlation). Eight textural layers were computed from the original image (Table3-5).

	was used to generate co-occurrence
	matrix. Four directions (0°, 45°, 90°, and 135°) of pixel neighborhood connections were
	considered. Texture descriptors were computed from GCLM. The Haralic et al. [42] equations

were used for texture measurements. These equations refer to 3 groups of descriptors that are: the contrast group (Contras, Dissimilarity, and Homogeneity), the orderliness group (Angular

Doctor of Philosophy -Kitsanai Charoenjit

Data & Methods/ 51 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data second moment,

Table 3 - 5

 35 Textural information equations.[START_REF] Haralick | Textural features for image classification[END_REF]

	Texture	GLCM	Formula	Description
	group	descriptor
		Contrast (CON)
	Contrast

Table 3

 3

	Doctor of Philosophy -Kitsanai Charoenjit

-1. The focal length of 2,989 mm and camera lens pixel size of 0.0065 mm were used for calibrating the image. Data & Methods/ 54 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

  in rows or types of land utilization and land cover

	Doctor of Philosophy -Kitsanai Charoenjit
	Xii	=	Oblique points in of Row i and Column i
	Xi+	=	All points in Row i
	X+I	=	All points in Column i
	N	=	Total points
	Denote : descriptive, K ˆ< 0 Less than chance agreement, 0.01-0.20 Slight agreement,
	0.21-0.40 Fair agreement, 0.41-0.60 Moderate agreement, 0.61-0.80 Substantial agreement, 0.81-0.99 Almost perfect agreement. RESULTS
			&
			DISCUSSION

Results & Discussion / 60 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 4 -

 4 2 and Figure 4-4.

	Equation (3-1), the Para rubber tree age at 25 years old is shown to have a maximum of
	biomass and carbon stocked (0.83 t, 0.37 tC) while a 4 years old is shown to have a minimum
	of biomass and carbon stocked (0.06 t, 0.03 tC). The Para rubber tree age 8, 12, 16, 18, 20 and

22 years old are stocked the biomass and carbon of (0.1 t, 0.04 tC), (0.21 t, 0.1 tC), (0.27 t, 0.12 tC), (0.36 t, 0.16 tC), (0.46 t, 0.21 tC) and (0.79 t, 0.35 tC) respectively. The rate of Doctor of Philosophy -Kitsanai Charoenjit Results & Discussion / 64 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data carbon sequestration, biomass and carbon stock obtained by a single tree stand are shown in

Table 4 - 2

 42 The rate capacity of carbon sequestration, biomass and carbon stock obtained by single tree stand of Para rubber tree.

	Class	Age (yr)	Biomass (ton)	Carbon (ton)
		4	0.06	0.03
	Young	8	0.10	0.04
		12	0.21	0.10
	Middle	16 18	0.27 0.36	0.12 0.16
		20	0.46	0.21
	Old	22	0.79	0.35
		25	0.83	0.37

Table 4 - 3

 43 Statistical of THAICHOTE satellite data. It was corrected radiometry using COST model (reflectance data)., SD = Standard deviation.

	Wavelength Min	Max	Mean	SD	Skewness	Kurtosis
	Blue	0	0.2550 0.0539	0.0375	-0.108	4.361
	Green	0	0.4299 0.0833	0.0590	0.081	4.182
	Red	0	0.6087 0.1259	0.0929	0.5	3.660
	NIR	0	0.8698 0.2227	0.1626	-0.185	4.178

Table 4 - 4

 44 The rate of carbon sequestration, biomass and carbon stock obtained by individual tree stand of Para rubber tree., Veg.1-3 is vegetation area 1-3.

		Blue	Green	Red		NIR
	Object	Non-Corrected	Corrected	Non-Corrected	Corrected	Non-Corrected	Corrected	Non-Corrected	Corrected
	Water	97.41	0.08	77.22	0.11	85.24	0.14	33.27	0.10
	Veg. 1	98.00	0.08	83.08	0.11	97.77	0.17	96.49	0.32
	Veg. 2	95.95	0.07	79.61	0.11	92.79	0.16	95.87	0.31
	Veg. 3	98.34	0.08	85.64	0.12	100.14	0.17	106.25	0.35
	Bare
	soil	149.09	0.12	152.51	0.22	236.88	0.42	130.33	0.43

Table 4 - 5

 45 Statistical of image classification layers obtained by spectral and textural information, For Spectral information: ARVI is Atmospherically Resistant Vegetation Index, GEMI is Global Environment Monitoring Index, IPVI is Infrared Percentage Vegetation Index, MSAVI2 is Modified Soil Adjusted Vegetation Index2 and NDVI is Normalized Difference Vegetation Index. For textural information (GLCM): CON is Contrast, DIS is Dissimilarity, HOM is Homogeneity, ASM is Angular second moment, ENT is Entropy, MEAN is Mean, VAR is Variance and COR is Correlation.

	Image Classification Layer	Min	Max	Mean	SD
	ARVI	-0.668	0.679	0.09	0.15
	GEMI	-0.611	0.699	0.187	0.237
	IPVI	0.042	0.928	0.632	0.079
	MSAVI2	0.167	0.85	0.427	0.138
	NDVI	-0.527	0.762	0.182	0.18
	CON	0	18.8	2.413	2.846
	DIS	0	9.47	0.9	0.746
	HOM	0	1	0.662	0.249
	ASM	0	1	0.374	0.43
	ENT	0	5	2.02	1.474
	MEAN	0	28.32	8.321	5.64
	VAR	0	7.4	1.16	2.2
	COR	-16.37	62.51	-2.64	35.44

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 4 -

 4 6a and 4-6b shows statistics of image characteristics measured (17 layers) at the emplacement where field data were acquired (see in Figure4-1) association to the girth and age of trees of each plantation. In mean, the spectral layers were slightly differentiated value while the texture layers were extremely differentiated value of each age class. In the textural information, Young age class was shown to the highest value of CON, DIS and VAR

	while Old age class was shown to lowest. The data distribution is descripted by frequency
	histogram on the skewness and kurtosis using mean. Most cases, the kurtosis value was
	shown to higher than 3.00 (normal distribution). Young age class, maximum skew was shown
	in CON (1.44) while minimum was shown in COR (-1.93) and maximum kurtosis was shown
	in COR while minimum was shown in HOM. Middle age class, maximum skew was shown in
	Red (1.17) while minimum was shown in COR (-1.26) and maximum kurtosis was shown in

Table 4 -

 4 6a Statistical of image characteristics measured at the emplacement where field data were acquired association to the girth and age of trees of each plantation (Min, Max, Mean, Standard deviation (SD)). For Spectral information : ARVI is Atmospherically Resistant Vegetation Index, GEMI is Global Environment Monitoring Index, IPVI is Infrared Percentage Vegetation Index, MSAVI2 is Modified Soil Adjusted Vegetation Index2 and NDVI is Normalized Difference Vegetation Index. For textural information (GLCM) : CON is Contrast, DIS is Dissimilarity, HOM is Homogeneity, ASM is Angular second moment, ENT is Entropy, MEAN is Mean, VAR is Variance and COR is Correlation.

	Layer	Min	Young class Max Mean	SD	Min	Middle class Max Mean SD	Min	Old class Max Mean SD
	Blue	0.06	0.09	0.08 0.00	0.07	0.08	0.08 0.00	0.07	0.08	0.08 0.00
	Green	0.10	0.14	0.12 0.01	0.11	0.12	0.11 0.00	0.11	0.13	0.12 0.00
	Red	0.13	0.22	0.17 0.01	0.15	0.17	0.16 0.01	0.15	0.21	0.17 0.01
	NIR	0.30	0.38	0.35 0.02	0.31	0.38	0.35 0.02	0.29	0.37	0.32 0.01
	ARVI	0.05	0.33	0.23 0.06	0.21	0.31	0.27 0.03	0.06	0.29	0.16 0.05
	GEMI	0.28	0.46	0.41 0.04	0.35	0.46	0.42 0.03	0.29	0.43	0.35 0.03
	IPVI	0.61	0.72	0.68 0.02	0.66	0.71	0.69 0.01	0.47	0.70	0.65 0.02
	MSAVI2	0.47	0.59	0.56 0.03	0.51	0.59	0.56 0.02	0.47	0.57	0.51 0.02
	NDVI	0.21	0.43	0.36 0.05	0.33	0.41	0.38 0.02	0.22	0.40	0.30 0.04
	CON	7.49	42.32	16.76 7.41	6.02 15.36	9.12 2.61	3.76 11.51	6.44 1.31
	DIS	2.10	5.59	3.20 0.73	1.89	3.00	2.32 0.34	1.49	2.74	1.93 0.20
	HOM	0.14	0.37	0.27 0.05	0.28	0.39	0.35 0.04	0.28	0.46	0.39 0.03
	ASM	0.01	0.03	0.01 0.00	0.01	0.03	0.02 0.01	0.02	0.05	0.03 0.01
	ENT	4.01	5.12	4.54 0.24	3.74	4.62	4.12 0.25	3.42	4.49	3.84 0.18
	MEAN	23.53	26.83	24.99 0.52 22.73 25.58 24.50 0.72 22.58 26.03 24.04 0.65
	VAR	3.96	15.36	7.92 2.75	2.98	8.85	4.90 1.41	1.99	7.43	3.51 0.84
	COR	-2.02	-0.29	-0.61 0.29 -0.75 -0.20 -0.35 0.13 -0.58 -0.08 -0.23 0.07

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 4 -

 4 6b Statistical of image characteristics measured at the emplacement where field data were acquired association to the girth and age of trees of each plantation (Skewness and Kurtosis). Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite DataFigure 4-19 The graphics obtained from mean, skewness and kurtosis of the image classification layers of each age class of Para rubber plantation. Broadband vegetation indexes: NIR is near infrared band, ARVI is Atmospherically Resistant Vegetation Index, GEMI is Global Environment Monitoring Index, IPVI is Infrared Percentage Vegetation Index, MSAVI2 is Modified Soil Adjusted Vegetation Index2 and NDVI is Normalized Difference Vegetation Index. Texture measurement: GLCM CON is Contrast, DIS is Dissimilarity, HOM is Homogeneous, ASM is Angular second moment, ENT is Entropy, MEAN is Mean, VAR is Variance and COR is Correlation.

	Layer	Young class Skewness Kurtosis	Middle class Skewness Kurtosis	Old class Skewness Kurtosis
	Blue	0.05	7.22	0.63	3.48	-0.27	3.70
	Green	0.22	5.77	0.71	3.11	0.34	2.99
	Red	0.65	3.63	1.17	4.12	0.38	2.67
	NIR	-1.03	4.25	-1.08	3.35	0.63	4.49
	ARVI	-0.75	2.91	-0.47	2.06	0.02	2.48
	GEMI	-0.99	3.70	-1.01	3.05	0.03	2.77
	IPVI	-0.95	3.74	-0.57	2.10	-1.73	12.66
	MSAVI2	-0.89	3.62	-1.04	3.13	0.23	3.08
	NDVI	-0.79	3.09	-0.57	2.10	-0.02	2.52
	CON	1.44	4.74	0.84	2.75	0.83	4.26
	DIS	1.08	3.75	0.52	2.24	0.52	3.85
	HOM	-0.31	2.48	-0.21	1.99	-0.15	3.24
	ASM	0.40	3.14	0.21	1.92	0.33	2.92
	ENT	0.34	2.72	0.20	2.08	0.18	3.35
	MEAN	0.00	3.82	-1.12	3.49	0.65	3.62
	VAR	1.12	3.70	0.84	3.34	1.30	6.34
	COR	-1.93	7.91	-1.26	4.25	-0.97	5.26

Skewness Yong class Middle class Old class

								Kurtosis
	12.00 14.00	Yong class	Middle class	Old class
	10.00
	8.00
	6.00
	4.00
	2.00
	0.00
		Blue	Green	Red	NIR	ARVI GEMI	IPVI MSAVI2 NDVI	CON	DIS	HOM	ASM	ENT	MEAN VAR	COR

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 4 - 8

 48 Model summary. The number of girth measurements is 388 samples. Method

	Stepwise regression, Criteria = Probability in(.05), Probability out(.10). Spectral information:
	NIR = Near-infrared band, RED = Red band, GEMI = Global Environment Monitoring Index,
	MSAVI2 = Modified Soil Adjusted Vegetation Index2. Textural information: HOMO =
	Homogeneous, DIS = Dissimilarity, CON = Contrast, VAR = Variance.
	Model	R 2	Adj.R 2 RMSE	Constant	Coefficient	Collinearity Statistics
				Tree
				girth (cm)			Tolerance	VIF
						NIR	-95.441	0.000 2,816.797
	TGM#1 0.531 0.526 11.40 -16.435	RED MSAVI2	73.826 55.867	0.001 0.000 2,877.383 759.217
						GEMI	18.064	0.001	693.770
						GEMI	-1.290	0.680	1.471
						HOMO	-2.740	0.005	183.046
	TGM#2 0.865 0.863	6.32	3.694	DIS	-0.933	0.001 1,167.832
						CON	0.068	0.002	497.873
						VAR	-0.015	0.056	17.926

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 4 - 9

 49 Excluded Variables. Spectral information: RED = Red band, NIR = Near-infrared band, ARVI = Atmospheric Resistance Vegetation Index, IPVI = Infrared Percentage

	Vegetation Index, MSAVI2 = Modified Soil Adjusted Vegetation Index2. NDVI =
	Normalized Different Vegetation. Textural information: COR = Correlation, ENT = Entropy,
	MEAN = Mean, ASM = Angular Second Moment.
				Collinearity Statistics
			Partial			Minimum
	Parameter	Sig.	Correlation	Tolerance	VIF	Tolerance
	BLUE	.105	-.083	.761	1.313	.001
	GREEN	.460	-.038	.763	1.311	.001
	RED	.997	.000	.484	2.067	.001
	NIR	.936	-.004	.210	4.752	.001
	ARVI	.972	.002	.093	10.789	.001
	IPVI	.688	.021	.065	15.489	.001
	MSAVI2	.903	.006	.014	72.879	.001
	NDVI	.688	.021	.065	15.489	.001
	COR	.836	-.011	.020	50.387	.001
	ENT	.733	-.017	.023	43.936	.001
	MEAN	.988	-.001	.666	1.501	.001
	ASM	.252	-.059	.048	20.871	.001

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 4 -

 4 -22).Estimation of Biomass andCarbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data while Middle age classes (from 12 to 18 years old) cover a surface of 48.68 km 2 (32%, 4,868 ha). The areas of each age classes are listed in Table 4-10. 10 Area summary of the Para rubber plantation.

	Old age classes (more than 18 years old) cover a surface of 54.55 km 2 (35%, 5,455
	ha), Young age classes (less than 12 years old) cover a surface of 51.12 km 2 (33%, 5,112 ha)

Table 4 -

 4 11 was shown the quality from object based classification of this study. A total 25,437 m 2 were used to compare data between object base and ground truth. The overall

	accuracy is 90% for all age classes and Kappa index is shown to almost perfect agreement
	(0.83).

Table 4 -

 4 11. Confusion matrix of Para rubber classification.

				Ground truth
			Young age	Middle age	Old age	Overall
	Object based	Young age Middle age Old age Overall	10,733 1,465 43 12,241	442 1,473 154 2,069	10 365 10,752 11,127	11,185 3,303 10,949 25,437
				Overall accuracy = 90.25%, Kappa = 0.83

Results & Discussion / 87 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

  111,656.32 tons (or 11.3%). The total amount of biomass stock in Mae Num Prasae watershed was estimated by the allometric equation (Equation 3-1). The highest biomass stocked in Para rubber plantations is obtained for 22 years old trees that sequester approximately 32% of the total biomass (703,766 tons). The lowest biomass stock is found at 4 years (approximate 0.1% or 1,276 tons) while at the age class of 20, 18, 12, 16, 8 and 25 years for the amount of biomass stocked is 23%, 20%, 14%, 10%, 1% and 0.1% respectively, (Table 4-12, Figure 4-23).The rate carbon stock of investigated area is average stocked 64 tC ha -1 . The age for 4 years old is lowest stocked biomass (11 tC ha -1 ) while the age for 25 years is highest stocked

	Doctor of Philosophy -Kitsanai Charoenjit
	(229 tC ha -1 ). The ages of 8, 12, 16, 18, 20 and 22 years old are expressed to 17, 33, 51, 67,
	86 and 112 tC ha -1 respectively, (Table 4-12, Figure 4-23). The carbon stock map can be
	drawn in Figure 4-24. The total biomass stocked in the study area is 2.23 Megatons
	corresponding to 0.99 Megatons of carbon stocked.
	The evaluation of CO2 sequestration by Para rubber age is reported in Table 4-12. The
	study found that Para rubber has the higher C sequestration at 25 years (33.53 tC ha -1 yr -1 ),
	while the lower C sequestration is found at 8 years (7.79 tC ha -1 yr -1 ). In 2011, the investigated
	area sequestered 33.05 tC corresponding to 121.28 t CO2 by Para rubber plantation assuming
	that 1 tC represents 3.676 t of CO2.

Results & Discussion / 90 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 4 -

 4 [START_REF] Iglesias | Determination of carbon sequestration and storage capacity of eucalyptus plantation in Sra Kaew Province[END_REF] Biomass and Carbon stocked in study area 2011. The uncertainty on the Carbon Stock comes from the uncertainty on the surface and the uncertainty of the model.

	Class	Age (yr)	Biomass Stock (t)	C Stock (tC)	Rate C Stock	(tC ha -1 )	Uncertainty of	C Stock (tC)	C Sequestered	(tC ha -1 yr -1 )	CO2 Sequestered	(tCO2 ha -1 yr -1 )
		4	1,275.55	567.88		10.91		72.16		2.73	10.01
	Young	8	34,309.54 15,274.61		16.99	1,845.45		2.12		7.79
		12	311,479.13 138,670.51		33.33	15,987.87		2.78	10.19
		16	223,062.92 99,307.61		50.53	11,363.43		3.16	11.59
	Middle	18	436,121.53 194,161.30		66.89	21,820.70		3.72	13.64
	Old	20	514,323.60 228,976.87		86.12	25,588.01		4.31		15.8
		22	703,766.39 313,316.80		112.25	34,847.56		5.1	18.73
		25	2,654.43	1,181.75		228.58		131.13		9.14	33.53
	Grand Total 2,226,993.09 991,457.32		64.23	111,656.32	33.05	121.28
					(average)	(11.3%)

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

  Despite the single bands in the spectral information are inappropriated to estimate biomass in Para rubber plantation, the vegetation indexes obtained by the Red and NIR combination have shown a high potential on forest biomass evaluation in agreement of Eckert, (2012)[START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF] and shown a substantial to extraction Para rubber surface by the vegetation indexes on the mask information.In the model TGM#2 (Table4-8), the texture parameters of homogeneity, dissimilarity and contrast were gave poor values of the VIF (Variance Inflation Factor) on the multicollinearity analysis as in agreement with the works of Eckert, (2012)[START_REF] Eckert | Improved forest biomass and carbon estimations using texture measures from WorldView-2 Satellite data[END_REF] and[START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF] [START_REF] Sarker | Improved forest biomass estimates using ALOS AVNIR-2 texture indices[END_REF] recommended to the good VIF value is less than ten. Thus, these texture parameters may unsuitable for tree girth and age prediction. In contrast, the empirical model TGM#2 obtained by five parameters (equation 4-18) is shown the R 2 of 0.87 higher than the model TGM#3 (R 2 of 0.82) when the GEMI (Global Environment Monitoring Index) and texture variance are considered. Thus in this studied, the empirical model TGM#2 is appropriated for connecting field data and remote sensing data and may show a limitation for publishing to the global model. The model summary of TGM#3 is shown in

	Doctor of Philosophy -Kitsanai Charoenjit
	The results of this study are in
	agreement with Eckert, (2012) [8] findings who observed the degraded forest stratum with
	high resolution WorldView-2 data. The textures are better parameters than spectral data to
	estimate the age of the canopy. The present work shows that the textural information has to be
	added to the spectral one for a precise inventory of characteristics of forest or plantations in
	agreement with the works of Eckert, (2012) [8] and Sarker et al., (2011) [9] with high
	resolution WORLDDVIEW-2 and ALOS AVNIR-2 used in Madagascar, Hong Kong and
	Central Siberia for forest biomass estimations.

Results & Discussion / 95 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 4 -

 4 [START_REF] Nilubol | Geo-Informatics Technology for Age Class Identification for Para rubber Plantation in Krabi Province[END_REF] Model summary of TGM#3. The number of girth measurements is 388 samples. ground biomass estimation using allometric relationship method varied as a function of tree components, tree species (clone of Para rubber) and study sites[START_REF] Shorrocks | Mineral nutrition, growth and nutrient cycle of Hevea brasiliensis. Growth and nutrient content[END_REF][START_REF] Wauters | Carbon stock in rubber plantation in Western Ghana and Mato Grosso (Brazil)[END_REF].

	Method Stepwise regression, Criteria = Probability in(.05), Probability out(.10). GEMI =
	Global Environment Monitoring Index, Textural information: VAR = Variance.
	Model	R 2	Adj.R 2 RMSE	Constant	Coefficient	Collinearity Statistics
				Tree
				girth (cm)				Tolerance	VIF
	TGM#3 0.817 0.816	7.07	1.462	GEMI VAR	-1.653 -0.04	0.917 0.917	1.091 1.091
	Several allometric relationship methods have been used for ground evaluation to
	biomass in Para rubber plantation. The allometric equation of Chuntuma et al., (2012) [7]

show high relationship in the same order magnitude (R 2 0.86 -0.99) to other works of

[START_REF] Shorrocks | Mineral nutrition, growth and nutrient cycle of Hevea brasiliensis. Growth and nutrient content[END_REF]

[START_REF] Shorrocks | Mineral nutrition, growth and nutrient cycle of Hevea brasiliensis. Growth and nutrient content[END_REF]

and

[START_REF] Wauters | Carbon stock in rubber plantation in Western Ghana and Mato Grosso (Brazil)[END_REF]

[START_REF] Wauters | Carbon stock in rubber plantation in Western Ghana and Mato Grosso (Brazil)[END_REF]

were tested in the biomass estimation of Hevea brasiliensis on Malaysia and Brazil respectively. Thus, the way in which Doctor of Philosophy -Kitsanai Charoenjit Results & Discussion / 96 Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

Table 4 -

 4 14 shows the capacity of carbon storage at rotation age (old age) in Eucalyptus (E.

Table 4 -

 4 [START_REF] Noiruksar | Application of Remote Sensing for Aboveground Carbon Storage Determination of Teak in Khun Mae Kham Mee Plantation, Prae province[END_REF] The rate of carbon storage at rotation age in Eucalyptus (E. camaldulensis), Teak (Tectona grandis) and Para rubber plantation RRIM 600 (Hevea brasiliensis).

Table A3 - 1

 A31 The input data set of TGM., Girth (cm), B1, B2, B3 and B4 are Blue, Green, Red and Near infrared bands (reflectance unitless)., ARVI is Atmospherically Resistant Vegetation Index., GEMI is Global Environment Monitoring Index., IPVI is Infrared Percentage Vegetation Index., MSAVI2 is Modified Soil Adjusted Vegetation Index2., NDVI is Normalized Difference Vegetation Index., txt_CON is texture Contrast., txt_Cor is texture Correlation., txt_DIS is texture Dissimilarity., txt_ENT is texture Entropy., txt_HOM is texture Homogeneity., txt_Mean is texture Mean., txt_ASM is texture Angular second moment., txt_Var is texture variance.

	Girth (cm)	B1	B2	B3	B4	ARVI	GEMI	IPVI	MSAVI	NDVI	txt_CON	txt_Cor	txt_DIS	txt_ENT	txt_HOM	txt_Mean	txt_ASM	txt_Var

Data & Methods/[START_REF] Kaufman | Atmospherically resistant vegetation index (ARVI) for EOSMODIS[END_REF]

m r = 10 m

Estimation of Biomass and Carbon Stock in Para rubber plantation in East Thailand Using Object-based Classification from THAICHOTE Satellite Data

The correlation between tree girth and values of the different layers obtained from the remote sensing image was tested by Pearson's correlation. All the Parameters of texture and vegetation indexes were correlated to the tree girth (Table 4-7, Figure 20). The Homogeneity has the highest correlation with tree girth (0.875) while lowest is the Mean (-0.496). For vegetation index, MSAVI2 has the highest correlation with tree girth (-0.663) while ARVI has the lowest (-0.512). For the single bands of the satellite image, NIR (near infrared) is highest correlated with tree girth (correlation coefficient of -0.679). Haga is a device to measure tree heights within fixed distances of 15, 20, 25 and 30m to the tree by the trigonometric principle and slope in percent. The suitable scale can be selected by rotating the adjustment disk at the front of the device. A reference tape can be installed at the tree to determine the distance opticaly.

Doctor of Philosophy -Kitsanai Charoenjit

A.4 Articles publication

The study has been published in 2 articles following as below:

• The partial result of the study has been published in e-proceeding on 14 th International Symposium on Water -Rock Interaction, June 2013., Avignon France. K.Charoenjit, P. Zuddas, P. Allemand, "Estimation of natural carbon sequestration in eastern Thailand: preliminary results." Procedia Earth and Planetary Science 1, 139-142 (2013). E-mail:kitsanai.charoenjit@upmc.fr, kitsanai@buu.ac.th