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REPORT. Analysis of the raw data of sample plots in NFIMAP - Cycle IV. From 2006 to USAID LEAF Program Analysis of Lam Dong

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REPORT Analysis of the raw data of sample plots in NFIMAP - Cycle IV From 2006 to 2010 USAID LEAF Program Analysis of Lam Dong a This report has been produced by the USAID-funded Lowering Emissions in
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REPORT Analysis of the raw data of sample plots in NFIMAP - Cycle IV From 2006 to 2010 USAID LEAF Program Analysis of Lam Dong a This report has been produced by the USAID-funded Lowering Emissions in Asia s Forests (USAID LEAF) program in its support for the development of the Lam Dong Provincial REDD+ Action Plan (PRAP). The report details the results of an analysis of the raw data of primary sample plots (PSPs) collected during Cycle IV of the National Forest Inventory and Monitoring Programme (NFIMAP) in Lam Dong, which was conducted in 2010 to support GHG emission factor estimations (EFs). It is one of five technical reports that have been developed to help the Lam Dong Department of Agriculture and Rural Development (DARD) in defining an appropriate Forest Reference Level FRL for the Province from which its policies and measures introduced to reduce emissions and increase greenhouse gas (GHG) removals from the forestry sector can be measured against. These studies will strongly support the on-going development of the Lam Dong PRAP. Authors Nguyen Dinh Hung, Peter Stephen, Tran Van Chau, Alexandre Grais, Silvia Petrova. The report is available through the USAID LEAF website at: For further details please contact: Ms Ly Thi Minh Hai USAID Lowering Emissions in Asia s Forests (LEAF) Viet Nam USAID LEAF Country Manager, SNV REDD+ Sector Leader 6th floor, Building B, La Thanh Hotel, 218 Doi Can, Ba Dinh, Hanoi, VIET NAM Telephone: +84 (4) /108 Dr David Ganz USAID Lowering Emissions in Asia s Forests (LEAF) Chief of Party Liberty Square, Suite Silom Rd. Bang Rak Bangkok 10500, THAILAND Telephone: +66 (0) Contents Introduction 5 1 Objectives, process and methods Objectives Process Data processing Data analysis Methods for data processing and analysis Calculation of missing tree heights Calculation of standing volume for individual trees Calculation of dry biomass Conversion from dry biomass to carbon stock Conducting statistics by land cover classes 11 2 Results and discussions Land categories Developing height curves and calculating missing tree heights Natural evergreen broadleaf forest Natural deciduous forest Natural coniferous forest Acacia hybrid plantations Pinus spp. plantations Cashew (Anacardium occidentale) plantations Calculation of missing tree heights Calculation of average criteria Total basal area/ha by land cover classes Timber volume/ha by land cover classes Carbon stock/ha by land cover classes 22 3 Conclusions and recommendations 23 USAID LEAF Program Analysis of Lam Dong i List of figures and tables Figure 1: Locations of the 95 primary sample plots in Lam Dong province 12 Figure 2: The height curve of the natural evergreen broadleaf forest 16 Figure 3: The height curve of the natural deciduous forest 16 Figure 4: The height curve of the natural coniferous forest 17 Figure 5: The height curve of Acacia auriculiformis plantations 17 Figure 6: The height curve of Acacia hybrid plantations 18 Figure 7: The height curve of Pinus spp. plantations 18 Figure 8: The height curve of cashew plantations 19 Table 1: List of field-based land categories of SSPs. 11 Table 2: List of map-based land cover classes of SSPs 13 Table 3: Summary of height curves used for calculating missing tree heights 18 Table 4: Average criteria by land cover classes 19 Table 5: Total basal area/ha by land cover classes 19 Table 6: Volume /ha by land cover classes 20 Table 7: Carbon stock/ha by land cover classes 21 ii Abbreviations DARD DBH GHG FIPI FRL LEAF MARD NFIMAP NRAP PRAP PSP REL SNV SSP RECOFTC REDD+ Department of Agriculture and Rural Development diameter at breast height greenhouse gas Forest Inventory and Planning Institute forest reference level Lowering Emissions in Asia s Forests Ministry of Agriculture and Rural Development National Forest Inventory, Monitoring and Assessment Program National REDD+ Action Program Provincial REDD+ Action Plan primary sample plot reference emission level SNV Netherlands Development Organisation secondary sample plot The Center for People and Forests Reducing Emissions from Deforestation and Forest Degradation USAID/ RDMA United States Agency for International Development / Regional Development Mission for Asia WD basic wood density USAID LEAF Program Analysis of Lam Dong iii Introduction Over the past decade, various national and international organizations have made significant efforts to work out mechanisms to combat deforestation and reduce emissions of greenhouse gases (GHG) from the forest and land use sectors. They have attempted to quantify different values of forest resources and forest environmental services and propose workable market payment incentive mechanisms so as to effectively manage these valuable resources. Among these efforts, the most prominent initiative is the Reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+) mechanism. This performance based mechanism is aimed at compensating developing countries for conserving and protecting their forest resources, thereby reducing GHG emissions and increasing GHG removals. REDD+ mechanisms also seek to generate additional social and environmental benefits, or multiple-benefits, which include biodiversity conservation, improvement of local livelihoods and gender equity. The United States Agency for International Development (USAID) funded Program Lowering Emissions in Asia s Forests (LEAF) is being implemented by Winrock International in partnership with SNV Netherlands Development Organisation, Climate Focus and The Center for People and Forests (RECOFTC) in six countries: Viet Nam, Laos, Cambodia, Thailand, Malaysia and Papua New Guinea. The purpose of the program is to strengthen the capacity of developing countries in the Asian region to produce meaningful and sustained reductions in GHG emissions from the forestry and land use sectors, thereby allowing these countries to benefit from the emerging international REDD+ program framework. 1 In Viet Nam, the USAID LEAF program was approved by the Ministry of Agriculture and Rural Development (MARD). USAID LEAF will provide support for the successful implementation of the Vietnam National REDD+ Action Program (NRAP). The province of Lam Dong has been selected as one of six pilot provinces under the NRAP to pilot REDD+. USAID LEAF is supporting the development of the Lam Dong Provincial REDD+ Action Plan (PRAP). Since 1990, Vietnam has implemented the National Forest Inventory and Monitoring and Assessment Program (NFIMAP) conducted by the Forest Inventory and Planning Institute (FIPI). Under the program, a huge dataset of forest resources has been collected and archived for the entire country. This report presents the methodology and outcomes of collating and analyzing the raw data of the fourth cycle of the NFIMAP ( ) in Lam Dong province. It is from analysis of this data that forest carbon stocks and emission factors have been derived for inclusion in the Lam Dong Forest Reference Level. 1 Objectives, process and methods 1.1 Objectives The main objective was to process and analyze the raw data of primary sample plots (PSPs) collected in Cycle IV of the NFIMAP in Lam Dong province to support emission factor estimation and consequently RELs/FRLs establishment for the PRAP in Lam Dong. 1.2 Process Data processing a. Data was converted from Microsoft Access format into Microsoft Excel format for analysis. The dataset included required parameters measured at the subplot, including: sample plot code number, subplot number, collection date, site description, coordinate of sample plots and subplots (VN2000), tree DBH, tree height, bamboo DBH and bamboo height. b. The subplots were grouped into datasets per forest class (rich, medium, poor) of forest types, plantation forest and bamboo forest (following MARD forest classification system - Circular 34/2009/TT- BNN) Data analysis All collected data was analyzed and the following outputs generated: a. Per secondary sample plot (SSP): range (minimum, maximum and median) of tree DBH and heights together with volume and carbon stocks of each SSP. b. Per dataset for each forest type: range of volume and carbon stock (minimum, maximum, and median) and statistical values of volume and carbon stock (standard deviation, error at 95% confidence level). 1.3 Methods for data processing and analysis Calculation of missing tree heights In each SSP, three normal trees nearest to the plot center were chosen for total height measurement. Based on data from these trees, the heights of other trees were estimated. The calculation was as follows: 3 Step 1: Based on the heights of measured trees, height curves were calculated using regression methods. The chosen model was the logarithm function, which is of the form: Hvn=a+b ln(dbh) (1) Where Hvn is the total heights of the trees, measured in meters (m), DBH is the diameter at the 1.3 m height position, expressed in centimeters (cm) and a and b are the coefficients of the function. Their optimal values can be found by using a regression method. In this case, the regression was conducted using Microsoft Excel software (the Data analysis tool in the Data menu). The height curves were calculated specifically for each forest type: evergreen broadleaf forest, deciduous forest, coniferous forest and plantations. For plantations, height curves specific to each species were developed if the number of sample trees for this species was 30. The forest types used for developing the height curves were based on the forest types collected in the field, not based on the forest types taken from the forest cover map. Step 2: The developed height curves were used to calculate the total heights for other trees that had not had their total heights measured Calculation of standing volume for individual trees In Vietnam, the standing volume of a tree is the volume of the stem from the base to the stem top. The calculation of standing volume for each fieldcollected forest type was as follows: a. Natural evergreen broadleaf forest The national two-parameter volume tables in the Forest Inventory and Planning Manual (FIPI, 1995) were applied. There are five national volume tables corresponding to five form groups. The volume table for form group 1 gives the smallest volumes and the volumes increase as the form group goes from 1 to 5. Here, the national volume table for form group 3 was used, since most of the tree species belonged to this group. To make the calculation easier an allometric equation equivalent to the volume table for form group 3 was used. The equation was: V = DBH 2 Hmt (2) Where DBH is the diameter at the 1.3m height position, in cm and Hmt is the length of the tree stem, in m. According to the Forest Inventory and Planning Manual (FIPI, 1995), the length along the tree stem can be calculated from the total tree height using the following formula: Hmt = Hvn 1.04 (3) USAID LEAF Program Analysis of Lam Dong 4 b. Natural deciduous forest The allometric volume equation specific to natural deciduous forest in the Central Highlands in the Forest Inventory and Planning Manual (FIPI, 1995) was applied: c. Natural coniferous forest V = DBH Hmt (4) The allometric volume equation specific to natural coniferous forest in the Central Highlands in the Forest Inventory and Planning Manual (FIPI, 1995) was applied: d. Plantations V = DBH Hmt (5) In Lam Dong province there were mainly four types of plantation: Pinus spp., Acacia hybrid, Acacia auriculiformis and Anacardium occidentale. For Pinus spp. plantations, the formula for calculating tree volume as specified in section (b) natural coniferous forest above was applied. For other plantations, the same standing volume equation as for natural evergreen broadleaf forest was applied Calculation of dry biomass a. For Acacia hybrid and Acacia auriculiformis plantations The allometric equations for calculation of dry biomass (including aboveground and below-ground biomass) for individual trees in these two plantations have already been developed (Hai, 2008). Acacia hybrid plantations The allometric biomass equation developed for Acacia hybrid was applied: B = AGB+BGB= DBH (6) Acacia auriculiformis plantations The allometric biomass equation developed for Acacia auriculiformis was applied: B = AGB+BGB= DBH (7) 5 Next, the sum of dry biomass of all individual trees in each SSP was used to calculate the total dry biomass of the SSPs. b. For other forest types Calculation of above-ground biomass for individual trees Natural evergreen broadleaf forest The allometric biomass equation developed specifically for evergreen broadleaf forest in Binh Thuan province (Hung et al. 2012) was applied: Natural deciduous forest AGB = DBH (8) The allometric biomass equation specific to deciduous forest in Binh Thuan province (Hung et al. 2012) was applied: Natural coniferous forest AGB = DBH (9) Since no allometric biomass equation specific to Vietnam coniferous forest was available, the indirect method of calculation via tree volume was used as follows: AGB = V BEF WD (10) Where BEF is the biomass expansion factor and WD is the basic wood density. Since the tree volume used in Vietnam is not the merchantable volume, the default BEF value provided in IPCC guidelines was not appropriate. In this study, the BEF of 1.3 (Brown, 1997) and the WD of 500 kg/m3 were used for coniferous forest. Bamboo forest The allometric biomass equation developed for Lo o (Bambusa balcoa) species in Binh Thuan province (Hung et al. 2012) was applied: Pinus spp. plantations AGB = DBH (11) The same method was applied as for natural coniferous forest. Cashew (Anacardium occidentale) plantations The same method was applied as for natural evergreen broadleaf forest. USAID LEAF Program Analysis of Lam Dong 6 Calculation of dry biomass for each SSP The total AGB of each SSP was calculated by summing up the AGB of all individual trees in the SSP. n AGB i = i AGB (12) ij j=1 Where AGBi is the AGB of the ith SSP, ni is the number of trees in the ith SSP and AGB ij is the AGB of the jth tree in the ith SSP. BGB was not calculated for each individual tree but was only calculated for each SSP. The calculation formula was as follows: BGB i = AGB i R (13) Where BGB i is the BGB of the ith SSP and R is the ratio between AGB and BGB. Here, the default values proposed by Mokany et al. (2006) were used. The value R = was applied for SSPs having AGB 125 tons/ha and the value R = was applied for SSPs having AGB 125 tons/ha. The total dry biomass of the ith SSP is equal to the sum of dry AGB and dry BGB of this SSP: B i =AGB i +BGB i (14) Conversion from dry biomass to carbon stock Carbon stock of SSP i was calculated using the following formula: C i = B i CF (15) Where Bi is the dry biomass (including above-ground and below-ground) of SSP i, measured in kilograms (kg) and CF is the carbon fraction factor. Here, the default value of 0.47 was used (McGroddy et al. 2004). The carbon stock value calculated as above is expressed in kg in an area of one SSP (i.e., 500 m 2 ). The following formula was used to convert to the unit of tons/ha: tc/ha i = C i x x500 = C i 50 (16) 7 1.3.5 Conducting statistics by land cover classes a. Calculation of the mean value The mean of volume or carbon stock for class i is the average value of all SSP on that class, that is: x i = 1 n i n i x (17) j=1 ij Where ni is the number of SSPs in class i and x ij is the value (basal area, volume, or carbon stock) of the jth SSP in class i. b. Calculation of coefficient of variation Coefficient of variation of class i was calculated by the following formula: S CV% i = i (18) x i Where S i is the standard error of all SSPs in class i and was calculated by the following formula: S i = li 1 l i l i (19) m ij l i -1 j=1 j=1 (y ij - x i.m ij ) 2 1. Where l i is the number of PSPs having at least one SSP of class i, y ij is the sum of values over all SSPs of class i in the jth PSP, x is the mean i value in class i and m ij is the number of SSPs of class i in the PSP j. c. Calculation of error (with the confidence level of 95%) The following formula for error calculation (with the confidence level of 95%) was applied for class i: E% i =t 05,li-1 CV% i (20) Where t 05,li-1 is the value of the t distribution of l i -1 degrees of freedom for the 95% confidence level. USAID LEAF Program Analysis of Lam Dong 8 2 Results and discussions 2.1 Land categories There were a total of 95 PSPs in Lam Dong province in Cycle IV- NFIMAP. The locations of these 95 PSPs in Lam Dong province are provided in Figure 1. Figure 1: Locations of the 95 primary sample plots in Lam Dong province As described in the parallel report titled Accuracy Assessment for Forest and Land Use Maps from , Lam Dong Province, Viet Nam, each PSP had 40 SSPs. Therefore, there were a total of 3,800 SSPs in Lam Dong in Cycle IV- NFIMAP. Based on the field data, they were in 63 different land categories, which were classified following Regulation No. 84. These 63 land categories are listed in Table 1 below. 9 Table 1: List of field-based land categories of SSPs. No Code Description 1 CAT Sandy land 3 2 CD Special-use land 34 3 CF Coffee DC Residence area 19 5 DIEU Cashew plantations 67 6 IA Bare land with grass 52 7 Ib Bare land with shrubs 65 8 IC Bare land with scattered trees 9 9 IIA Young forest without volume stock IIA+L Young forest without volume stock + bamboos IIB Young forest with volume stock IIB+L Young forest with volume stock + bamboos IIB+M Young forest with volume stock + bamboos IIIA1 Evergreen broadleaf forest - poor IIIA1+L Evergreen broadleaf forest - poor + bamboos IIIA1+M Evergreen broadleaf forest - poor + bamboos IIIA1+TH5 Evergreen broadleaf forest - poor + matured Pinus trees 1 18 IIIA2 Evergreen broadleaf forest - medium IIIA2+L Evergreen broadleaf forest - medium + bamboos IIIA2+M Evergreen broadleaf forest - medium + bamboos 6 21 IIIA3 Evergreen broadleaf forest - rich (IIIA3) IIIA3+L Evergreen broadleaf forest - rich + bamboos IIIA3+N Evergreen broadleaf forest - rich + bamboos 7 24 IIIB Evergreen broadleaf forest - rich (IIIB) IVA Evergreen broadleaf forest - primary KL.I Acacia hybrid plantations - age class I 3 27 KL.II Acacia hybrid plantations - age class II KLT.I Acacia auriculiformis plantations - age class I 7 29 KLT.II Acacia auriculiformis plantations - age class II L.IIa Bamboo forest 3 31 L.IIIA Bamboo forest 6 32 L.IIIb Mixed wood - bamboo forest MN Water bodies NI+IIA Small bamboos + regrowth forest 1 No of SSPs USAID LEAF Program Analysis of Lam Dong 10 No Code Description 35 NN Agriculture land RAY Slash and burn fields RI Deciduous ecosystem with scattered trees 6 38 RII Young regrowth deciduous forest RII+TH5 Young regrowth deciduous forest + matured pinus trees 2 40 RIIIA1 Deciduous forest - poor RIIIA2+L Deciduous forest - medium + bamboos RIIIB+L Deciduous forest - medium + bamboos RIIIB+Le Deciduous forest - medium + bamboos Th21 Coniferous forest - young small trees Th22 Coniferous forest - young large trees Th22+RII Coniferous forest - small trees + young deciduous forest 13 No of SSPs 47 Th31 Coniferous forest - medium-aged small trees Th31+RIIIA1 Coniferous forest - medium-aged small trees + deciduous forest - poor Th32 Coniferous forest - medium-aged large trees TH32+RIIIA1 Coniferous forest - medium-aged large trees + deciduous forest - poor 51 TH4 Coniferous forest - nearly matured Th5 Coniferous forest - matured Th5+IIB Coniferous forest - matured + young forest Th5+IIIA1 Coniferous forest - matured + wooden forest - poor 1 55 Th5+RIIIA1 Coniferous forest - matured + deciduous forest - poor 5 56 ThI Pinus plantations - age class THII Pinus plantations - age class ThII+BDII Pinus plantations - age class 2 + Eucalyptus robusta plantations - age class ThIII Pinus plantations - age class ThIII+TIII Pinus plantations - age cla
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