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MINISTRY OF EDUCATION MINISTRY OF AGRICULTURE AND TRAINING AND RURAL DEVELOPMEN VIETNAM NATIONAL UNIVERSITY OF FORESTRY BAKHAM CHANTHAVONG APPLICATION OF GEOSPATIAL TECHNOLOGY FOR FOREST RESOURCE MANAGEMENT IN THE NAM KA DINH NATIONAL PARK AREA, LAOS PEOPLE DEMOCRATIC REPUBLIC MAJOR: FOREST RESOURCE MANAGEMENT CODE NO: 9620211 SUMMARY OF PhD DISSERTATION HANOI, 2022 This scientific work has been accomplished at Vietnam National University of Forestry Supervisor: Assoc Prof Dr Phung Van Khoa Assoc Prof Dr Sithong Thongmanivong Reviewer 1: ……………………… Reviewer 2: ………………… Reviewer 3:.………………… This Doctoral dissertation will be defended in front of the University Doctoral Committee at Vietnam National University of Forestry, Hanoi, Vietnam at … date …/…/2022 This dissertation can be found at: - National Library of Viet Nam - Library of Vietnam National University of Forestry PREFACE The Urgency of dissertation topic Laos is a country located in the tropics, with a natural land area of 23,680,000 hectares, forestry land accounting for 47% of the country's area Annually, the rate of forest changes is higher than the general average (2.5%) (MAFL, 2018) The process of change spread across regions, districts and provinces in which forests cover across the country In order to limit the fluctuation of forest resources, the Lao government has established a number of national parks and conservation areas in order to manage and sustainably exploit natural forest resources Nam Ka Dinh National Park (NKDNP), Bolikhamsay province was established in 1995, with a total natural land area of 168,550ha with forest types divided by species composition (Statistical Yearbook of Bolikhamsay province, 2020) ) The area of natural forest in the National Park fluctuates with deforestation (MR) and forest degradation (STR) constantly increasing, causing the total area of natural forest to gradually decrease, the level of variation decreases by about 2.5%/year ( Bolikhamsay Department of Agricultura and Forestry DARB, (2020) The question: Why is the area of natural forest still fluctuating? How to determine that level of volatility? Currently, forest changes are often detected directly by the staff and organizations, forest owners and local people, etc., along with the strong development of information technology, which must be mentioned Geospatial technology has played an important role in detecting and assessing changes in forest resources In Laos, the geospatial technology is increasingly being applied in the field of management, monitoring and assessment of changes in forest resources such as: forest inventory and survey; monitoring forestry activities (afforestation, logging, etc.) However, because it has not been widely applied in assessing changes in forest resources in some national parks and protected areas, including NKDNP, and there is little understanding of the current status of forest resources in the district, The main causes of fluctuations have not been identified as a scientific basis for forest management solutions, so the sustainable management of forest resources here is facing many difficulties, notably: - It is not possible to determine the extent of changes in forest resources over time; - The main factors causing the fluctuations have not been identified soon; - Solutions for applying geospatial science and technology in forest resource management have not yet been proposed In order to contribute to solving the above-mentioned problems, we conducted the doctoral dissertation topic "Application of geospatial technology for forest resource management in the Nam Ka Dinh National Park Area, Laos People Democratic Republic" This conducted is very necessary and has high theoretical and practical significance Objectives 2.1 General objective: Application of geospatial technology to early detect deforestation and forest degradation contributes to improving the efficiency of forest management and protection and monitoring of forest resources changes in the Nam Ka Đinh National Park Area, Lao PDR 2.2 Specific objectives (1) To determine relative index threshold for detecting deforestation, forest degradation and assessing accuracy; (1) To propose the application of geospatial technology for early detection of deforestation, forest degradation and regrowning in the Nam Ka Đinh National Park Area Subjects application of geospatial technology in monitoring and early detection of deforestation, forest degradation and regrowning forest and solutions to promote the application of geospatial technology, improve the efficiency of forest management in the study area Scientific and practical significance of the dissertation - Scientific significance of the dissertation: The dissertation contributes to upplementing the relative index threshold reflecting the change of remote sensing index with the atmospheric resistant vegetation index (ARVI) on Sentinel satellite images as a scientific basis for the development of satellite images early deforestation, forest degradation and regrowing forest in the area of Nam Ka Dinh National Park, Lao People's Democratic Republic - Practical significance of the dissertation The research results of the dissertation initially proposed the technical process of applying geospatial technology in forest resource management in Nam Ka Dinh National Park, Lao People's Democratic Republic from satellite images Sentinel New finding of dissertation The dissertation has identified the relative index threshold for early detection of deforestation, forest degradation and regrowing forests in the Nam Ka National Park area with the use of the ARVI spy on Sentinel Dissertation structure In addition to the introduction and conclusions, references, appendices, the dissertation is structured into chapters A total of 120 pages of dissertation, of which: Introduction - pages; Chapter 1: Literature review – 20 pages; Chapter 2: Contents, research methodologies and Natural conditions of the Research area - 23 pages; Chapter 3: Results and discussion - 73 pages; Conclusion, existence, recommendation pages; References pages In this dissertation, there are 34 tables, 29 figures CHAPTER LITERATURE REVIEW The dissertation has referred and summarized about main related issues: (1) The concept of deforestation; forest degradation anh regrowed forest; (2) Scientific basis of geospatial technology; (3) Research and application of geospatial technology in forest resource management in the world, Vietnam and in Laos PDR About the concept of deforestation; forest degradation anh regrowed forest Overview of research problems has helped to correct and comprehensively aware of The concept of deforestation; forest degradation anh regrowed forest About Scientific basis of geospatial technology The overview of the research problem has helped to have a correct and comprehensive understanding of the scientific basis of Geospatial Technology Accordingly, the scientific basis of Geospatial Technology is a combination of many technologies applied in forest resource change assessment and management to meet the purposes of sustainable management and business About Research and application of geospatial technology in forest resource management in the world, Vietnam and in Laos PDR The research problem overview helped identify outstanding achievements in using geospatial technology to assess variability Outstanding Achievements about: - Measures using post-classification comparison techniques to determine changes in forest resources over time in the world, in Vietnam and Laos - Measures using change detection algorithm to identify changes in forest resources over time in the world, in Vietnam and Laos About limitation of the research Although many achievements have been made, researches on the application of natural forest rehabilitations in the world still limited, which can summarize a number of key issues Technology application has not been able to cover and exploit to the fullest the advantages brought by the geospatial technology system, the system of remote sensing image documents, new studies have only focused on using a number of remote sensing technologies remote sensing, GIS software and a number of remote sensing image documents, popular remote sensing indexes such as: Landsats; SPOT photo; MODIS photo; Sentinel satellite images and some remote sensing indexes such as: NDVI, NBR For some research of that in Laos, it is possible to summarize some of the main shortcomings about the application of geospatial technology in forest resource management in general and in the Nam Ka Đinh National Park in particular as follows: - There is little/no application of geospatial technology in defining characteristics and changes in forest (D, FD and FR) forest resources over time; - The main factors causing changes in forest resources have not been identified; - The main causes of D, FD and FR have not been classified and analyzed; Identify research problems for this dissertation The main products of this dissertation are: to define boundaries, demarcate and mark the functional subdivisions and buffer zones of the NKD National Park; clearly demarcate the planned land area for purposes other than those planned for the NKD National Park in order to harmoniously develop forest protection, environmental protection, nature conservation and conservation of wildlife species endangered, precious, rare Proposing a process to guide the monitoring, inspection and monitoring of forest resources and biodiversity in the NKDNP in order to effectively conserve and prevent deforestation, encroachment, and illegal conversion of use purposes in the NP However, with the content of the thesis topic, the main approach of the topic is: Applying geospatial technology in forest resource management in the National Park to provide periodic and regular information on: (i) Deforestation and degradation of forest resources; (ii) Regrowing forest area This research dissertation has profound scientific and practical significance in order to overcome the shortcomings of previous studies, contributing to providing a technological process to ensure effective management and monitoring of forest resources CHAPTER CONTENTS, RESEARCH METHODOLOGIES AND NATURAL CONDITIONS OF THE RESEARCH AREA 2.1 Research contents 2.1.1 Research on the current status of forest resources and factors affecting the management of forest resources in the Nam Ka Dinh National Park area (a) Characteristic of the structure of forest plant communities (b) factors affecting the management of forest resources (c) Infrastructure for applying geospatial technology in forest resource management in the area of NKDNP 2.1.2 Determining relative index threshold to detect deforestation and forest degradation in the Nam Ka Dinh National Park (a) Building datasets on deforestation and forest degradation: b Determination of remote sensing index threshold c Assessing the accuracy of detecting 2.1.3 Determining relative index threshold to detect regrowing forest in the Nam Ka Dinh National Park (a) Building datasets on deforestation and forest degradation: b Determination of remote sensing index threshold c Assessing the accuracy of detecting 2.1.4 Proposing the applicable direction of geospatial technology to detect deforestation and forest degradation and regrowing forest in the NKDNP Area (a) Proposing the applicable direction of geospatial technology to detect deforestation and forest degradation and regrowing forest in the NKDNP Area (b) Proposing a number of support measures to improve the efficiency of using geospatial technology in forest resource management in the NKDNP Area (c) Proposing solutions to improve the efficiency of forest management and protection in the study area 2.2 Research method 2.2.1 Methodology Scientific basis: The scientific basis of optical remote sensing image source is the spectral reflectance of natural objects, which can be analyzed and shown, especially can detect and separate the areas of deforestation with separate regions Based on the spectral reflectance characteristics of objects, by specialized models and software, remote sensing image data is processed to identify and separate each object Multi-temporal remote sensing image source allows to quickly determine changes in forest cover in general and forest loss in particular in the time interval between the time of image acquisition After extracting information about forest objects, it is possible to create maps of the current status and calculate specific areas at each time as well as synthesize and analyze results on distribution and changes Atmospheric Resistance Vegetation Index (ARVI) represents the quality of green vegetation on the ground, this index value ranges from -1 to +1, the higher the value, the thicker the vegetation For forest objects, this index is quite high (about 0.6 to 1), when this value is reduced, plants are lost, or in other words, forests are lost Similar to other vegetation indices, the index's value will represent vegetation quality in one way or another - Selection of research data + Method of extracting ARVI value for sample points on imagery: ARVI value is extracted at sample points on satellite imagery using Extract Multi Values to Points tool in ArcGIS 10.3 software + Used satellite imageries: the dissertation uses Sentinel satellite imageries, Sentinel satellite imageries are downloaded from GEE The satellite imagery has been converted to surface spectral reflectance value (SR) These imageries include Bands Cloudy pixels in the imageries have been reverted to “No Data” through the commands in GEE Experience in the world, in Vietnam as well as in Laos shows that almost no remote sensing index (NDVI; NBR; SAVI; ARVI; IRSI; NDSI; EVI) has outstanding advantages compared to other indicators other under all conditions Therefore, the application of remote sensing indicators depends on the specific conditions of each region as well as the existing measurement database system in that region Applying The findamental points of the dissertation, a diagram of the research approach of the dissertation is shown below More details were explained in following sections: Figure 2.1 An overview of the research methodology 2.2.2 Specific research methods The methods are presented according to each research content of the thesis 2.2.2.1 Methods of investigation and assessment of forest status and forest fires in the study area (i) Determine the type of forest / forest status distributed in the area: Classification of forest types/states is determined according to the classification system prescribed in the Lao Forestry Law 2019 (Laos National Assembly, 2018) (ii) Methods of characterization of forest states In order to fully collect the characteristics of the forest structure and status, the thesis sets up survey routes The survey route is a typical route (typically according to the type of forest), representative of the forest types, the length of the route is unknown (according to the length of the forest type) The extensive field surveys were conducted with munbers typical standard forest plots 2.2.2.2 Determining relative index threshold for detection of deforestation and forest degradation on Sentinel imageries in the NKDNP (1) Collection and Analysis of Secondary Data Secondary data and information were collected by reviewing the relevant literature and documents obtained from Provincial and District Agriculture and Forestry Offices in Borlikhamxai, NKDNP offices and other agencies We also obtained data on community forest and forest encroachment from the Department of Forestry, Ministry of Agriculture and Forestry Statistical data and information is obtained from District Agriculture and Forestry Offices and village committees Sentinel Satellite dataset from 2016 and 2019 were used to map deforestation and forest degradation areas for the two periods and analyze the trends in forest area and its condition The Satellite images were freely downloaded from the Google Earth Engine (GEE) Two scenes were acquired (T48QVF and T48QUF) to cover the entire landscape of the study area The image scenes were ortho-rectified with the help of ground control points and a digital terrain model to remove the distortions arising from variations in topography, and then projected into Universal Transverse Mercator 1984 zone 48N (WGS_1984_UTM_Zone_48N) datum The images were classified through visual interpretation Due to limitation and time availability for the classification and insufficient ground truth data The analyses were carried out in GEE and ArcGIS software (2) Collection and Analysis of Primary Data * Collection of deforestation and degradation samples: Samples of deforestation and forest degradation were collected in the NKDNP during the period from 2016 to 2019 Selected a total of 212 deforestation samples The total number of selected forest degradation samples is 90 samples 75 The samples as shown in Table 2.2 Table 2.2 The mumber of samples to determine the threshold and assess the accuracy Sample to Sample to assess TT Subject Code Samples determine the the accuracy threshold Deforestation MR 212 162 50 Forest degradation STR 75 56 19 (3) Relative Index The ARVI index images in the study area are collected directly on GEE to save processing time and data storage space, calculated based on the formula: Relative index is calculated by formula as follows: ARVI = [NIR - (2 × RED) + BLUE] / [NIR + (2 × RED) + BLUE] (2.4) The dissertation has used relative KB index to determine the threshold: forest degradation, deforestation through the use of remote sensing indicators (ARVI)) KB (ARVI) = (ARVIT2 - ARVIT1) × 100 / ARVIT1 (2.5) Where: T1 is the value of the remote sensing index at the time before the impact T2 is the value of the remote sensing index at the time after the impact 2.2.2.2 Determining relative index threshold for detection of regrowing forest in the NKDNP The samples as shown in Table 2.3 Tables2 Structure of the sample plots with the investigated forest: No Couse changed Munber Forest rehabilitation post fire 18 Forest rehabilitation post log 10 Forest rehabilitation post planted tree log 20 Forest rehabilitation post shift cultivation 16 Forest planted in bare land and grassland 16 Totally 80 The dissertation has used relative KB index to determine the threshold: regrowing forest through the use of remote sensing indicators (ARVI)) KB (ARVI) = (ARVIT2 - ARVIT1) × 100 / ARVIT1 (2.5) Where: T1 is the value of the remote sensing index at the time before the impact T2 is the value of the remote sensing index at the time after the impact 2.2.2.4 Methods of assessing the accuracy Methods of assessing the accuracy of detecting deforestation and forest degradation The thesis uses 150 deforestation samples and 90 deforestation samples in 2018 in the Central Highlands Percentage detected (%) = the number of detected examples the total number of examples 𝑥100 (10) Where: M is rate of difference in area (%); n is the total number of examples; A t is the realistic area (ha); Ft is the detected area in the imagery (ha) 2.2.2.5 Proposing the applicable direction of geospatial technology to early detection of deforestation and forest degradation and regrowing forest in the NKDNP The results of establishing a scientific basis and determining the threshold of relative KB index for early detection of deforestation and forest degradation are used as the basis for proposing the direction of applying geospatial technology to early detection of deforestation, forest degradation and regrowing forest in the NKDNP 2.3 Characteristics of the NKDNP area - Natural conditions of the research area The NKDNP is located in the Northwest region of Bolikhamsay province, about 173 km north-west of Vientiane, the capital of Laos (18015’–18055’N; 103049’–104031’E))(Figure 2.6) The total natural area is 808 hectares Figure 2.6 Geographic location, boundaries and area of NKDNP - Climate, soil and hydrological Annual mean temperature ranges from 20,2°C to 22,5°C The 11 years mean temperature during survey was recorded at 21,1°C The total mean rainfall ranges from 124,5mm month-1 Rainfall is concentrated from May to November, accounting for 70 -75% of annual rainfall The NKDNP has two main stream systems They are headwaters of two rivers in the area: Pui River, Phun River and Loop River, Nham River There are many small streams, relatively high density, distributed evenly over the area of the national NKDNP The stream flow is often greater in the rainy season, and during the dry season they are not dry but have low water flow The geological background of NKDNP is formed from the following four rock groups: acid magma rock group, mainly granite rocks; neutral alkaline magma group, mostly basalt; shale group, mainly clay schist, mica schist and accreting matter group along streams, mostly new alluvium Brown-red feralit soil developed on neutral alkaline magma rock: About 40% of the total areas are these soils They often appear at Eastern slopes of NPNP range The soil layer is thick, slightly acid with PHkcl The geological formations consist mainly of a yellow - red lateritic loamy soil derived from quartz with pH varying between and The hills around the plain consist mainly of sandstone, granite, and schist, with medium - rich loams - Biological resources The original vegetation cover of the area consisted primarily of Dipterocarps Forest; Mixed Deciduous Broadleaf Forests; Semi - Mixed Deciduous Broadleaf Forests; The forest types correspond approximately to the Dipterocarps Forest (mainly Dipterocarpaceae Fabaceae), Mixed Deciduous Broadleaf Forests (mainly Fabaceae) and (mainly Leguminosae) 11 Planted No types forest Unit year Hvn (m) D1.3 (cm) G (m /ha) Mbq (m3/ha) Quality (%) Good quality Bad quality Tectona grandis 14,70 21,45 (±4,65 (±6,62) 27,21 (±4,63) 89,433 (±6,22) 87,55 (±9,12) 12,54 (±0,91) Hevea Brasiliensi 10,43 11,58 (±3,67 (±2,25) 10,609 (±5,55) 63,775 (±5,93) 87,55 (±9,12) 12,54 (±0,91) Hevea Brasiliensi 13,75 14,78 (±4,43 (±5,33) 15,955 (±6,15) 81,877 (±6,07) 79,54 (±11,77) 20.46 (±3,56) Acronyms in table 3.2, G: Mean basal area ha-1 ( m2/ha-1); M: Mean stand volume -1( m3/ha-1) D1 3: Mean of diameter at breat height per year (cm/year -1); Hvn: Mean of height per year (m/year -1 );∆G: Mean variation of basal area per year (m2/year -1ha ) (± SD = standard error) With the current forest status of the NKD National Park's resources and the characteristics of a number of indicators of tree layer structure according to the main natural forest type, the type of degraded forest is studied as a basis and the basis is not only used as a sample area for extracting Exporting remote sensing index values, but also helping to calculate and interpolate the relative threshold of atmospheric resistant vegetation index KB (ARVI) to detect areas of regrowing forest (c) Infrastructure for applying geospatial technology in forest resource management in the area of NKDNP 3.1.3 Current status of infrastructure for geospatial technology application in Nam Ka Dinh National Park 3.1.3.1 Current status of infrastructure - No LAN system and no server system - The Nam Ka Dinh National Park Management Board has an Internet connection - Connection line: ADSL line type Transmission line providers: ETL and Mphone Number of transmission lines 02 Broadband (Kbps) - Personal computer system - Private email system (personal) - There is no website address about Nam Ka Dinh National Park With the results on the current state of the above infrastructure, it is an important foundation in the application of geospatial technology systems, the number of computers, network connection lines and desktop configurations to meet technical requirements minimum for the application of geospatial technology in forest resource management in NPKNP 3.1.3.2.Current status of application software Current status of application software and usage status are summarized in Table 3.3 12 Table 3.3 List of software to support management in Nam Ka Dinh National Park Staff using Status Applicability assessment Staffs Well done Well done Staffs Well done Well done MicroStation No available No available No available MapInfo No available No available No available ArcGIS No available No available No available ENVI No available No available No available TT Titles of software Word 2019 Professional Plus Excel 2019 Professional Plus (Sources; The authors surveyed and computing, 2020) Specialized software, serving geospatial technology such as ArcGis; EVNI; Mapinfo; Qgis, etc., have not been applied in forest resource management in the National Park This is one of the limitations and lack of foundation for the application of geospatial technology in forest resource management at the Nam Ka Ding National Park Management Board 3.1.3.3 Current status of staff for geospatial technology application development The management board has been assigned 16 staffs A total of 16 staffs are recruited and are working in the Management Board, including ranks, qualifications and training expertise, which are listed in Table 3.4 Table 3.4 Qualifications and training expertise of officials and employees of the NKDNP Board Management Qualifications No Unit Number Board management Department of Accounting Administration Department of Forestry Engineering Department of Forest Protection Major Degree certificate Master; bachelor Office Informatics B Forestry Bachelor Office Informatics Economics and Business Administration Bachelor Bachelor Office Informatics B Office Informatics Forestry Forestry (Sources; The authors surveyed and computing, 2020) Most of the staff have expertise in forestry, except for the Accounting-Administration Department In the management board, there are no officers or employees who are trained in 13 information technology or have training certificates in information technology operations 3.1.4 The main factors affecting the management of forest resources in the NKDNP area 3.1.4.1 Directed drivers in the NKDNP area (i) Limitations of the management and protection of forest resources related to natural, socio-economic conditions - The management and protection of forest resources face many difficulties due to difficult terrain, difficult traffic, inadequate facilities and equipment Furthermore, the NKDNP area has a large population dependent on the forest - Propaganda to raise awareness of people and local authorities at all levels is still limited and still formal - The force for management and protection of forest resources in the area has been organized systematically, but the efficiency is not high, professional teams have not yet been built (ii) Limitations of the management and protection of forest resources related to science and technology - The technical and professional capacity of the force for forest protection and management and protection of forest resources in the area of the National Park is not very high and has not been properly trained - Forest rangers are assigned to be the core in the management and protection of forest resources, but they are very thin and scattered; professional qualifications in management and protection of forest resources are still limited - The forest area of the Park is large, but the works for the management and protection of forest resources are very few (iii) Logging, especially high-value trees, causes forest degradation - Exploitation of non-timber forest products: Non-timber forest product exploitation takes place all year round with the level of exploitation depending on the availability of each area bordering the National Park - Exploiting firewood, burning coal, grazing livestock, fishing with destructive equipment, encroaching on forest land for cultivation, etc It is also one of the direct threats affecting the environment, significantly reducing the quality of forests At the time of investigation, the author of the thesis directly encountered cases of illegal logging and box-cutting right in the mixed evergreen semi-deciduous forest, images of loggers were recorded at the scene Figure 3.2 Figure 3.2 Image of loggers exploiting and sawing boxes of hopea odorata 14 (iv) Land conversion and encroachment on forest land causes deforestation The second reason is converting forest land to swidden cultivation, even converting to industrial crops (growing rubber trees) (v) The movement to plant trees and create forests in the locality and in the National Park In order to support forest protection and development, address the causes of deforestation in the Nam Ka Dinh National Park area and localities in the region, thereby reducing emissions due to deforestation, forest degradation and increasing water absorption Due to the restoration and regeneration of forests, the Lao Ministry of Agriculture and Forestry and the Bolikhamsay Department of Agriculture and Forestry have been implementing the Emission Reduction Program in North Central Laos (vi) Well implement the project of zoning, promoting natural habitats and protecting natural forests Thanks to the well-implemented implementation of the project, the National Park protects, develops and uses effectively and sustainably the existing forest area, contributing to meeting the requirements of disaster mitigation, ecological environment protection, and response to natural disasters Climate Change 3.1.4.2 Indirected drivers - Demand for firewood and timber: The main reason, derived from the practical, daily needs of households and people, needs a source of firewood to cook food for daily meals - Lack of employment: Lack of employment is the second most influential cause of forest resources here - Lack of arable land or arable farmland of households with low productivity, pests and destructive livestock is the third cause, accounting for over 60% of households - Lack of foods - Macro policy of the Party, State of Laos and the people's government of Bolikhamsay province - Low income The majority of households believe that their household income is low compared to the common level in the region and in comparison with the whole country of Laos 3.2 Application of remote sensing index threshold in early detection of deforestation and forest degradation in the area of National Park 3.2.1 Draw polygon samples in the Sentinel satellite Polygon sample locations in the field servey are used for visual interpretation based on high-resolution remote sensing data, delineating sample areas for research, some plotted sample areas are shown in the figure 3.4 and figure 3.5 Figure 3.4 Deforestation polygon samples located: 15 Point (1) XY = (353204; 2061092); Point (2) XY = (351389; 2058540) Sentinel color composite image RGB: 12-8A-4) in the Google Earth Engine at the time before (ASentinel 2B on February 24, 2017) and after (B-Sentinel 2B on April 14, 2017) when the forest was deforested Figure 3.5 Forest degratation polygon samples located: Point (1) XY = (491062; 1988120); Point (2) XY = (351389; 2058540) Sentinel color composite image RGB: 12-8A-4) in the Google Earth Engine on GEE at the time before (A-Sentinel 2A on April 9, 2016) and after (B-Sentinel 2A on April 4, 2017) when forest degradation 3.2.2 Determine threshold of remote sensing index and verify the results The ARVI index data layers are calculated for specific times, clearly showing the index fluctuations in the polygon sample areas of deforestation and forest degradation The independent variable of the ARVI index is shown in the figures below Figure 3.6 The ARVI index image of sample area examples before (A) and after (B) when deforested, and corresponding KB(ARVI) index image (C) The results clearly show the change of the ARVI index on the deforested areas, both in terms of the value of the index and the display (Figure 3.5A, B) Thereby, the KB index (ARVI) has a clear difference between the deforested areas and the surrounding areas (Figure 3.5C) In forest degradation samples, the color change displayed on the ARVI image is not really clear, the pixels have a clear change in focus, leading to the display sample area not having outstanding contrast, the result It is shown in figure 3.7 16 Figure 3.7 The ARVI index image of sample area examples before (A) and after (B) when forest degradation, and corresponding KB(ARVI) index image (C) Through calculating the KB index (ARVI) of the deforested and degraded sample areas, the results of determining the relative index threshold of the deforested sample areas are summarized in Table 3.3: Table 3.5 Statistical characteristics of the threshold sample areas standard Sample Numbers Min index Mean Max index error Deforestation 162 4,661 - 88,764 - 75,603 - 65,770 Forest 56 5,416 - 29,831 - 18,569 - 5,441 degradation Totally 218 The data and results summarized in Table 3.5 showed that: (1) Of the total 162 samples studied on deforestation, the KB(ARVI) index has the lowlest value -88.76 and the hightest value -65.77 and the mean value -75.60 (2) Out of a total of 56 forest degradation research samples, the KB(ARVI) index has the lowest value of -29.83 and the hightest value of -5.44 and the average value of -18.56 Since then, the study has determined the threshold to detect deforestation in the study area for the case of using ARVI index and Sentinel image with KB(ARVI) from -88.76 to 65.77 And the threshold to detect forest degradation in the study area has KB(ARVI) from 29.83 to 5.44 3.2.3 Accessed Accuracy The results show that: the method using the relative index KB with the use of Sentinel images to detect forest loss has the following accuracy: the detection accuracy of deforestation is 98.0% and the inference accuracy is 98.0% forest degradation is 84.2% The verification results show that the thresholds for determining deforestation have a high accuracy rate and are promising to be applied in practice For the threshold for determining forest degradation, the accuracy rate is relatively high, but additional research is needed on the number of samples to give more convincing results of thresholding and verification 17 3.2.4 To map of the distribution of deforestation and forest degradation The distribution map of areas of deforestation and forest degradation is shown in Figure 3.8 Figure 3.8 Map of the distribution of deforestation and forest degradation in the NKDNP Area From the current map of deforestation and forest degradation established above, using tools in GIS software to calculate and statistic the area of each forest type in the current map of forest resources established by the method Using remote sensing index threshold, calculate and statistic the area of each forest status in NKDNP 3.2 Discussion 3.2.5.1 About the threshold of the relative index of detecting deforestation and forest degradation The study has determined the threshold of the relative index KB to determine forest degradation and deforestation with values respectively: the study has determined the threshold to detect deforestation in the study area for the case of using the ARVI index and Sentinel images have KB(ARVI) from -88.76 to -65.77 And the threshold to detect forest degradation in the study area has KB(ARVI) from -29.83 to 5.44 Soils without vegetation often have ARVI values