Nghiên cứu đề xuất giải pháp phòng cháy cho rừng thông ba lá (pinus kesiya) tại vườn quốc gia bidoup núi bà, tỉnh lâm đồng tt tiếng anh

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Nghiên cứu đề xuất giải pháp phòng cháy cho rừng thông ba lá (pinus kesiya) tại vườn quốc gia bidoup   núi bà, tỉnh lâm đồng tt tiếng anh

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MINISTRY OF EDUCTION AND TRAINING MINISTRY OF AGRICULTURE AND RURAL DEVELOPMENT VIETNAM NATIONAL FORESTRY UNIVERSITY Le Van Huong RESEARCH OF WILDFIRE AND PREVENTING SOLUTIONS FOR PINUS KESIYA FORESTS AT BI DOUP-NUI BA NATIONAL PARK IN LAM DONG PROVINCE Major: Forest Resource Management Code: 962021 SUMMARY OF DOCTORAL DISSERTATION IN FORESTRY MINH CHAU HANOI, 2019 The research has been completed at: The Vietnam National Forestry University, Xuan Mai, Chuong My, Hanoi Scientific instructors: Assoc Prof Be Minh Chau Assoc Prof Tran Ngoc Hai Examiner 1: Examiner 2: Examiner 3: This doctoral dissertation will be defended at the VNUF-level Board of Examiners at ………………………………………………………………… …………………………………………at………on……………… This doctoral dissertation can be found at: - National Library of Vietnam - Library of Vietnam National University of Forestry, Hanoi ABSTRACT Necessity Combustion is a physicochemical process that produces heat energy through the oxidation of organic matter [83] A fire can only occur when there is a simultaneous combination of three basic elements that make up the fire triangle, namely oxygen, burning materials and heat sources [11] In nature, the process of wildfire is more complicated than that of a simple burning of single organic matter, because all three factors (oxygen, flammable material and heat source) change rapidly over time and space When the fire reaches a certain intensity and magnitude, the fire spreads across the entire landscape, burning most of the biomass of vegetation on the surface of the forest land In recent years, wildfire continuously occur in the United States, Russia, Greece, Australia, Brazil, Indonesia, etc leaving enormous socio-economic and environmental consequences In Vietnam, wildfire also occurs frequently, but the extent of damage is often not fully documented Lam Dong province is located in the crucial region where wildfire mostly takes place With the characteristic climate of the Central Highlands of Vietnam, wildfire often occur in the dry season from November to April each year Lam Dong Provincial FPD reported 544 wildfire from 2001 to 2017 that caused loss of 1,413.96 of forest of various types [12] Bidoup Nui Ba National Park (BNBNP), a member of Vietnam’s national park network, covers an area of 70,038 The park is the core zone of the World’s Lang Biang Biosphere Reserve and it is also recognized as an ASEAN heritage park where has global values of biodiversity However, one of the biggest challenges that BNBNP currently faces is wildfire According to the 2014 forest inventory results [34], the total area of flammable forest in BNBNP 30,930 The statistics also show that from 2005 to 2017, 84 wildfire happened in BNBNP with the damaged area of 229.4 ha, mainly pine forest Since the landscape consists of typical climatic factors of three-needled pine trees, think grasses, flammable material of up to 20 ton per after years without wildfire, fire is likely to occur any time When wildfires take place on a large scale, globally significant biodiversity and landscape values can be severely damaged and difficult to be recovered Many scientific studies on wildfire prevention and fighting have been conducted in the world and in Vietnam The research targets how to minimize the damage caused by wildfire Many countries have applied modern technologies in wildfire prevention However, wildfires are still frequent around the world as well as in Vietnam and they are also considered one type of natural disaster in the context of global climate change Thus, there are still many challenges in the study of wildfire Currently, research results still fail to meet the objectives of wildfire prevention and fighting at management levels and managers, including BNBNP It raises the question on how to minimize possible damage caused by wildfire in BNBNP where is considered at hot spot of wildfires in Lam Dong province For such reasons, the selection and implementation of the Research on Wildfire and Preventing Solutions for Pinus kesiya forest at Bidoup – Nui Ba National Park, in Lam Dong province is highly necessary Goals of the research 2.1 General goals A scientific research to propose effective solutions for fire prevention and fighting in three-needled pine (Pinus kesiya) forest at Bidoup – Nui Ba National Park, in Lam Dong province 2.2 Specific goals (1) Identify the major characteristics of the three-needled pine Pinus kesiya forest in related to wildfire (2) Determine the composition of flammable material of the three-needled pine forest and their relationships in the forest environment (3) Identify scientific foundations for wildfire prevention (4) Propose effective solutions for fire prevention and fighting in three-needled pine forest at Bidoup – Nui Ba National Park Objects and Scale of the study 3.1 Objects Three-needled pine planted forest and natural forest of BNBNP, in Lam Dong province 3.2 Scale The study focuses on four areas at different altitudes, including: Dung Kno; Dung Iar Jieng, Cong Troi and Bidoup in BNBNP of Lam Dong province Contributions of the study Theorically, the thesis has quantified and developed mathematical models on the correlation between flammable material, temperature, humidity of forest environment and possibilities of wildfire as a basis for forecasting the danger of wildfire Practically, the thesis has: - Proposed a new method of classification of flammable material and the burning coefficient K in assessing and forecasting the danger of wildfire - Determined wildfire season and classifying wildfire dangers by univariate and multivariate statistical models - Determined objects, intensity and time of prescribed burning; - Proposed effective solutions of preventing wildfire for BNBNP based on scientific and practical foundations Scientific and practical values 5.1 Scientific values The thesis studies the correlation between factors related to wildfire, quantifies them as a basis for building models of forecasting dangers and season of wildfire, and provides sound foundations for treating flammable material and prescribed burning in three-needled pine forests in BNBNP 5.2 Practical values The thesis has proposed methods for forecasting wildfire dangers and season and effective solutions for wildfire prevention in three-needled pine forests in BNBNP Chapter OVERVIEW Based on our review of 95 nationally and internationally published papers related to the following topics: (1) Distribution characteristics of the three-needled pine forest and wildfire; (2) Characteristics of flammable material and the danger of wildfire; (3) Methods for forecasting wildfire and (4) Technical measures for wildfire prevention, the following conclusions are confirmed: (1) The distribution area, ecological conditions, biological characteristics of the species show that the formation and existence of three-needled pine forest are related to the occurrence of wildfire Studies have also confirmed that the three-needled pine forest is annually flammable (in the dry season) (2) Flammable material (fuel) is one of three factors formulating the wildfire triangle Flammable material is connected with wildfire under the following indicators: Ingredients and types of material, height, weight, size, spatial arrangement on the forest ground and moisture Other environmental impacting factors are exposure direction, wind speed, temperature, humidity and forest environment temperature, If oxygen and heat are excluded, flammable material has been the main objective of research on wildfire prevention and fight (3) Aridity is often used to determine the wildfire season, possibly for a specific area They are usually set by rainfall, temperature, humidity and evaporation (4) Wildfire danger indices are often used to predict the likelihood of wildfire They are different from the aridity indices, which are often set on meteorological factors Some fire danger indicators also take into account the volume, humidity, accumulation ability of flammable material, etc of target forests (5) Remote sensing technology is increasingly widely used in forecasting and detecting wildfire However, early detection of wildfire by remote sensing technology for fire fighting is very challenging due to the nature of forestry The industry manages a large forest area, so it is difficult to access the fire site immediately when wildfire occurs (6) Traditional measures of wildfire prevention including clearings, green belt, guarding, awareness of forest visitors have been continuously utilized by foresters (7) In addition to traditional measures, prescribed burning is widely applied This is an effective approach to reduce the volume of flammable material that causes wildfire However, there have been no quantitative studies to acknowledge the timing and intensity of, and status of forest suitable for prescribed burning for effective prevention and fighting Answering the above questions is scientifically sound to propose burning solutions for fire protection in BNBNP and different fired-forest ecosystems Chapter METHODOLOGY 2.1 Contents (1) Characteristics of three-needled pine forests and wildfire in BNBNP; (2) Characteristics of flammable material in three-needled pine forests; (3) Modeling the correlation among components of flammable material; (4) Forecast of wildfire danger in BNBNP; (5) Proposed solutions for wildfire prevention in three-needled pine forests in BNBNP 2.2 Approaches The study applies systematic and ecosystem approaches 2.3 Approach chart Major ecological factors Causes of wildfire Identification of independent and dependent variables Environmental factors and flammable material Algorithms Proposal of solutions 2.4 Research methodology 2.4.1 Database (i) The 1st field survey collected data in 150 plots to serve the author’s Masters degree Results are presented as one of crucial parts in this dissertation Table 2.1 Time, location and types of forest in the 1st field survey No Sub-zone Date of survey Type of forest/year of plantation Level of aging Number of plots 148B 58 26 125 129 and 130 15/12/2009 30/01/2010 05/02/2010 07/02/2010 01/02/2010 Planted forest (PR) 1997 PF 2001 PF 1997 PF 1998 Natural forest III II II III 30 30 30 30 30 Total 150 (ii) The 2nd field survey collected data in 340 plots, directly serving this study Table 2.2 Time, location and types of forest in 340 plots No Sub-zone Date of survey Type of forest/year of plantation Level of aging Number of plots 10 11 12 13 14 26 103 76 59 80 96C 100 102A 75B 93 145A 27 145A 124 31/12/2015 17/01/2016 25/01/2016 01/02/2016 28/02/2016 06/03/2016 30/11/2016 18/02/2017 19/02/2017 19/02/2017 20/11/2015 29/12/2015 30/01/2016 23/03/2016 PF 2002 PF 2002 PF 1996 PF 1998 PF 1996 PF 1997 PF 2011 TR 1999 PF 1999 PF 1998 Natural forest Natural forest Natural forest Natural forest III III IV IV IV IV I III III III 40 30 30 30 15 35 45 15 15 15 10 27 30 340 Total (iii) Data collected from 25 verification plots in Cong Troi Area Table 2.3 Database of verification experiments for forecasting models Month T (oC) H (%) m1 (Kg) K Nov Dec Jan Feb Mar 27 29.26 25.04 25 30.24 51.4 55.14 73.66 53.4 28.12 0.85 1.58 2.4 5.93 1.54 0.141346 0.652034 0.542221 0.85001 0.52215 2.4.2 Methodologies of field survey and data treating 2.4.2.1 Methodology of field survey and experiment - Study forests are categorized by altitude and origin of forests Planted forest is classified by age level of I, II, III and IV, five year for each level - 1st survey: 150 round-shaped were systematically sampled to inventory basic parameters of forests of the level of age I, II, III and IV using the 6-tree methods Within these plots, 150 subplots of m2 (2m x 2m) each were surveyed for floristic composition and burning material and burned to collect basic variables related to wildfire - 2nd survey: 340 round-shaped plots and subplots were additionally experimented in the same manner for the same parameters and variables, including temperature and moisture The experimental layout is as follows: Figure 2.2 Experiment layout 2.4.2.2 Data collection - In round-shaped plots, trees were surveyed with D1,3, Hvn and N using the 6-tree method, where r6 = a6 + d6/2 (r6 is plot radius, a6 distance from plot center to the 6th tree and d6 tree diameter at breast height) Data collected were to understand the structural characteristics of three-needled pine forest that are related to wildfire in BNBNP - In the m2 experimental plots, surveys were focused on plant composition to find out the origin of burning material and their classification and volume, measuring temperature, humidity and conducting experimental burning to collect data for modeling possibilities of wildfire The collected data are as follows: +) Height measurement, identification and sampling of plants under forest canopy +) Survey weight and composition of flammable material: Separating naturally dried flammable material with size ≤ cm collected by scraping and measuring (to the nearest of 0.1 g) the weight of original natural dry material (m1, kg/4m2) Living material (shrubs with a root diameter of less than cm, grass, vines) were cut at the base by sickles and the weight of the original living material was measured (m2, kg/4m2) The total weight of flammable material under the forest canopy (denoted as M, kg / 4m2) is calculated as follows: M = m1 + m2 The inflammability index or flammability index K is calculated by the formula K = m1/ M +) Mix well the pile and burn to record the burning time, denoted as Tc after burning, collect and weigh the remaining material, and determine the percentage of burned material, denoted as Pc +) Use portable PCE-HT110 measuring device to measure the temperature (T, oC) and the humidity in the experimental plot (H,%) to determine the temperature and humidity in the environment at research time The data is only collected from 10 am to 14 pm on non-rainy days and does not record the temperature and humidity directly in the sun light 2.4.2.3 Data treating methods - Identification of plant species The data and samples collected in the field were identified using Pham Hoang Ho's Flora of Vietnam (1999)[18] with assistance from forest plant experts - Modeling the correlation among components of flammable material Based on the collected data of the indicators m1, m2, M, Tc, Pc and K, modelling is made using the correlation matrix of components to choose the best model, following Nguyen Ngoc Kieng (1993), (1996)[24], [25] - Determining wildfire season and forecasting wildfire dangers + Analysis of meteorological database Meteorological parameters by monthly average (from January to December) such as temperature (T, o C), maximum temperature (Tmax, oC), minimum temperature (Tmin, oC), air humidity (H,%), rainfall (P, mm), sunshine hours (Sm, hours) in the period of 1978 - 2014 provided by Da Lat Meteorological Station The heat amplitude (dT, oC) is calculated using the formula dT = Tmax - Tmin and sunshine hours in a day (S, hours) by the formula S = Sm / N, where N is the number of days of the months + Gaussen - Walter chart (based on P and T) with additional meteorological factors including, H, dT and S + Calculation of aridity indexes [75]: + Lang Index: LANG = P / T + De Martonne Index: DEMA = 12 * P / (T + 10) + Selyaninov index: SELY = P / (0.1 * + Ivanov Index: IVA = P / E, with E = 0.0018 * (25 + T) * (100 - H) + Thornthwaite Index: THORW = P / PET, with PET = 16 * (10 * T / I) a * (S / 12) * (N / 30),  T), where  T is the total temperature of the month where: and a = 6,75*10-7*I3 – 7,71*10-5*I2 + 1,792*10-2*I + 0,49239 + The wildfire danger index is calculated as following: (1) Angstrom Index: ANGS = (H / 20) + (27 - T) / 10 (2) Sharples Index: SHAR = 10 - 0.25 * (T - H) (3) Cheney-Sullivan Index: SUL = 9,668 - 0,207 * T + 0,137 * H (4) Viney index: VIN = 5,658 + 0.0465 * H + 3,151 * 10-4 * H3 * T-1 - 0,1854 * T0,77 - Multivariate statistics: + To determine the wildfire season, the following methods were used: Principal Component Analysis (PCA), Factor Analysis (FA), Multi-Dimensional Scaling Analysis (MDSA) and Cluster Analysis (CA) + Establishing the set of independent variables {T, H, m1, K} and of dependent variables {Tc, Pc} in Canonical Correlation Analysis (CCA) to determine the canonical correlation coefficient R (R =  with  is eigenvalue) and probability level of significance P + Discriminant Functions Analysis (DFA): Establishing the Canonical Discriminant Functions CDF and Fisher Classification Functions FCF and calculating Mahalanobis distances with database Wildfire danger was predicted using Mahalanobis distance and Fisher classification function models Chapter RESULTS AND DISCUSSION 3.1 Some characteristics of three-needled pine forest and wildfire status in BNBNP 3.1.1 Three-needled pine forest distribution in BNBNP Analysis of input database of BNBNP shows 23,545 of three-needled pine forest, including 21,498 of natural forest and 2,047 of planted forest The lowest point with the appearance of threeneedled pine is 646 m, the highest point 2,200 m Natural three-needled pine forest is distributed in 70 subzones while planted forest covers 2,047 scattered in 30 sub-zones Three-needled pine forest is classified into four areas with different altitudes: (1) Dung K’no area: altitude from 630 - 1,000 m, areas of 2,016 ha; (2) Dung Iar Jieng area: height from 1,000 m to 1,400 m, area of 10,012 ha; (3) Cong Troi area: height from 1,400 m - 1,900 m, area of 10,970 ha; (4) Bidoup area: altitude of 1,900 m - 2,087 m, areas of 541 3.1.2 Some characteristics of the planted three-needled pine forest Analysis results from 290 plots of three-leaf pine planted forest from age I to age IV in BNBNP draw the following results: - Planted forests at the age I to IV have distinct differences in height, diameter and density This shows that the uneven quality of planted forest in BNBNP - The height difference of grass at all ages is insignificant, so planted forest at all age levels are equally flammable - There is a correlation between the density of planted forests and the height of grass, usually where with high density and low grass and vice versa 3.1.3 Some characteristics of the natural three-needled pine forest Analysis of 100 plots of natural three-needled pine forests shows that natural three-needled pine forests are rather even and rich in forest stock This is also consistent with the results of the 2014 forest inventory database in BNBNP with the total areas of rich and average coniferous forest to be up to 60% of the total coniferous forest areas The same holds true for planted forests as analysis shows a correlation between height of grass and density of forest trees If density is high, the height of the grass is low and vice versa 3.1.4 Wildfire in Bidoup-Nui Ba National Park Records by Lam Dong Forest Protection Department [12] in the period of 2005-2017 in Lam Dong province show 544 fires, damaging 1,413.96 of forest of all kinds According to the Forest Protection Department of BNBNP, from 2005 to 2017, 84 wildfire happened in BNBNP, damaging and affecting 229.4 of natural and planted forests 3.1.5 Causes of wildfire The results of semi-oriented interviews show that causes of wildfire in BNBNP include: burning farms accounting for highest proportion (24.6%), followed by flammable material burning/irregualry prescribed burning (23.5%) Accidental fire by foresters or tourists account for the lowest (6.5%) 3.2 Characteristics of flammable material 3.2.1 Definition, classification and basic properties of flammable material Based on a ecological factor viewpoint, the concept, classification and properties of flammable material are proposed as follows: (1) Concept: flammable material are all plants and their fallen objects (2) Classification: flammable material are divided into the following two categories: - Dry material includes: + Dried up trunks, branches and leaves of the vegetation + Falling objects of the forest + Surface soil which is yet decomposed - Living material includes all species included in the living composition of the vegetation (3) Properties of flammable material: Flammable material has the following remarkable properties: (i) dry material and living material are related to each other according to the growing season, biological characteristics of the species (ii) flammable material continuously changes under direct or indirect impacts of ecological factors, in which the meteorological and hydrological factors are decisive to the flammability of the material (iii) When a new fire emerges, living materials prevent the possibility of wildfire (iv) When a wildfire occurs at a certain intensity, living materials will change into dry material and the whole material will burn 3.2.2 Composition of flammable material The survey shows that 288 vascular plant species belonging to 76 families are part of flammable material in three-needled pine forest and such plants have many different growth forms Based on the biological characteristics of each species and the objectives of fire prevention, criteria of classifying threeneedled pine forest plants are classified into three groups: (1) fire-resistant species, (2) flammable species and (3) highly flammable species The results of the analysis are compiled into a list of all plants involved in the fire 3.2.3 Flammable plants Based on the established plant list, criteria for classifying less flammable species, flammable species and highly flammable species The study has cataloged 39 species of highly flammable plant species in three-leaved pine forests in BNBNP 3.2.4 Weight and flammability of flammable material The composition of the weight of flammable material including weight of dry material (m1), weight of living material (m2) total weight of combustible material (M) and Inflammability index (K ) from 490 study plots for forests of age groups I, II, III and IV were aggregated into tables as input database to analyze their correlation with the possibility of wildfire 3.2.5 Correlation matrix of components of flammable material The correlation matrix of components of flammable material in three-needled pine forest has been established Analyzing the matrix shows that: (i) m1 and m2 show a negative correlation (in Cong Troi: r = - 0.66653; P = 0,0001

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