Sustainable Forest Management in Changing Climate. Support to National Assessment and Long Term Monitoring of The Forest and Tree Resources in Vietnam

Transcription

1 Sustainable Forest Management in Changing Climate FAO Government of Finland Forestry Programme Multi-donor trust fund: GCP/GLO/194/MUL Support to National Assessment and Long Term Monitoring of The Forest and Tree Resources in Vietnam Project no: GCP/GLO/194/MUL/(FIN)-VN Technical Report: Overview of Improved NFIMAP Methodology July 2013 Mr. Tani Höyhtyä, Dr. Nguyen Dinh Hung, Mr. Ngo Van Tu, Mr. Ho Manh Tuong FIPI, VNFOREST, MARD 1 P a g e

2 Table of Contents 1. NFA project overview Objectives Activities Outcomes... 5 Partners NFI s in international framework and FAO s role in it NFA project framework, linkage to other forestry programmes Improved NFIMAP design for Vietnam Analyzing NFI cycles I IV sampling design and data collection Development of improved field measurements and testing in the field Nationwide sampling design Accuracy and cost comparison of different sampling designs Total number of clusters and plots needed for nationwide NFIMAP implementation Comparison of NFI 4 and NFIMAP differences The principles and expected outputs of improved NFIMAP programme Expected outputs of future NFIMAP programme Utilization of satellite images as part of NFIMAP Programme Data input, verification, validation and result calculation Resources needed and estimated costs Proposal for NFIMAP national framework P a g e

3 Acronyms CC CART DEM DMC FAO FAOR FIPI FLEGT FORMIS FRA GHG GIS GO GPS IPCC KIA MARD METLA MONRE MRV NFA NFI NFMA NFIMAP NGO NWFP NRSC PDA PSP REDD RS SFM ToF UN UNFCCC USD UTM VNFOREST VN2000 Climate Change Classification and regression trees Digital Elevation Model Disaster Management Constellation Food and Agricultural Organization of The United Nations FAO Representative (in member countries) Forest Inventory and Planning Institute Forest Law Enforcement, Governance and Trade Support Programme Development Of Management Information System For Forestry Sector Forest Resources Assessment Green House Gas Geographic Information Systems Governmental Organization Global Positioning System Intergovernmental Panel on Climate Change Kappa coefficient of inter-rater agreement Ministry of Agriculture and Rural Development Finnish Forest Research Institute Ministry of Natural Resources and Environment Monitoring, Reporting and Verification National Forest Assessment (Project) National Forest Inventory National Forest Monitoring and Assessment National Forest Inventory, Monitoring and Assessment Programme Non-governmental Organization Non-wood Forest Product National Remote Sensing Center Personal Digital Assistant, mobile device Permanent Sample Plot Reducing Emissions from Deforestation and Forest Degradation Remote Sensing Sustainable Forest Management Trees Outside of Forests United Nations United Nations Framework Convention on Climate Change United States Dollar Universal Transverse Mercator Vietnam Administration of Forestry Vietnamese Coordinate System 3 P a g e

4 1. NFA project overview The Food and Agriculture Organization of the United Nations (FAO) offers assistance to Viet Nam to develop its capacity in forest and tree resources assessment over a period of three years, starting from March The National Forest Assessment (NFA) project is part of a global programme entitled Sustainable Forest Management in Changing Climate launched by FAO. The project is implemented within 3 years from March 2011 by Forest Inventory and Planning Institute (FIPI) with the supervision of Vietnam Administration of Forestry (VNFOREST) under Ministry of Agriculture and Rural Development (MARD). The project aims to enhance the capacity of the Viet Nam Forestry Administration and to introduce new and appropriate technologies. At the same time, it will help Viet Nam in reviewing forest inventory parameters against emerging national and international reporting requirements (incl. REDD+). In addition, the project will contribute to meeting the country s demand for sustainable forest management, as well as efforts to cope with adverse impacts of climate change and protecting biodiversity. The project is one of 5 pilot countries of the FAO- Finland Forestry Programme. The total project budget is US$3 3,2 million for , of which the Government of Finland through the FAO- Finland Forestry Programme funded US$ 2.7 million and the contribution of Vietnam Government is US$ 489, Objectives The main objective of NFA is to assist MARD/VNFOREST in the development of the National Forest Inventory and Monitoring Programme (NFIMAP) through following activities: 1. Strengthen institutional capacity of Vietnam Administration of Forestry (VNFOREST), Ministry of Agriculture and Rural Development (MARD), focusing on Forest Inventory and Planning Institute (FIPI) and other implementing institutions; 2. Harmonise and update the information on forests and trees and related use and users; 3. Consolidate the monitoring system of the resources; and 4. Provide information for the review the forestry sector policy in the light of the results from the forest resources assessment. 1.2 Activities Assessment of information needs, available existing NFA related information, requirements and definition of inventory objectives including the integration with the NFIMAP objectives; Assembling of available information to support the design of the inventory, planning of the field survey, including sampling design, preparation of field and mapping manuals, purchase of equipment and capacity building; Data collection through field survey and satellite image interpretation/analysis of digital imagery, gathering of reference material; Processing and analysis of the collected data and publication of findings. Establishing the Framework program on assessment and long-term monitoring of forest resources in Vietnam. 4 P a g e

5 1.3 Outcomes 1. Established broad consensus at the national level on the needs and approach to NFIMAP in Viet Nam by taking into account national users requirements and country s obligations to reporting to international processes, including REDD+; 2. Capacity of VNFOREST and FIPI strengthened to collect, analyse and disseminate information on forest resources, users and uses; 3. Prepared bases to develop national forest and land use maps at levels and scales based on harmonised classification of forest and land uses and related definitions that serves also REDD+ monitoring and the development of the national Forest Management Information System (FOMIS); 4. National assessment of the forest and trees outside forest resources operational. 5. Framework established for a long term monitoring of the forestry resources. Partners Forest Management Information System Project (FORMIS) National Forest Inventory and Monitoring Programme (NFIMAP) Finnish Forest Research Institute, METLA, Finland based on the completed ICI project with FIPI and LoA within the FAO FIN Programme REDD+ related initiatives e.g. UNFCCC, the Intergovernmental Panel on Climate Change (IPCC), UN REDD Programme Bilateral donors NGOs 5 P a g e

6 2. NFI s in international framework and FAO s role in it We need information on forest resources in various levels for decision making and management purposes starting from individual forest owner until decision makers of global community. During the past decades and under the increasing threats of changing climate, people all around the world begin to realize that we are not alone. Decision and actions of each individual land owner or country have their impact to global stability of environment and climate change. Information on: Extent of forest resources Biological diversity Forest health and vitality Protective functions of forest resources Productive functions of forest resources Socio-economic functions of forest resources Institutional and legal framework Forest land Village-Local community National Level Global community Picture 1: We need information of forest resources as part of global community. When the FAO was established, one of its core functions was to collect, analyze and disseminate information on agriculture, forestry and fisheries. This is still the case and corner stones from the simple but powerful belief that better information leads to better decisions, which lead to better actions. FAO has been monitoring the world's forests at 5 to 10 year intervals since The Global Forest Resources Assessments (FRA) are now produced every five years in an attempt to provide a consistent approach to describing the world s forests and how they are changing. The Assessment is based on two primary sources of data: Country Reports prepared by National Correspondents and remote sensing that is conducted by FAO together with national focal points and regional partners. Currently, 22 countries worldwide have repeated NFI s in place and 45 countries have sometimes implemented NFI. For 84 countries, the global forest resources assessment is based on remote sensing data analyses only. 6 P a g e

7 Picture 2. The knowledge of national forest resources world-wide status based on measurement strategy and information sources. Only 22 countries have repeated NFI in place. FAO tries to support national NFI s during the whole process from designing the inventory method until calculation and analyses of final results to answer national and international data needs. The typical chain of events is presented in Picture 3 below. US$0.3 3M, 2-3 years Design Field Implementation Data Processing Reporting Policy Analysis Help design projects respond to country s needs Assist in recruiting international staff Participate & run workshops Help in training of national staff Provide technical guidance to national team to carry out the NFi according to best approach. Assist in developing and installing database Train national staff in db use Assist in data entry and editing/validation Assist in data analysis Assist in report Help in writing to fit agreed format of national reports Technically clear reports triggering and stimulating national policy analysis FAO Forestry Capacity development / FAO assistance Picture 3. The role of FAO in capacity building and support to national programmes. 7 P a g e

8 3. NFA project framework, linkage to other forestry programmes There have been four rounds of national forest inventories in Vietnam since 1990 (NFIMAP = National Forest Inventory, Monitoring and Assessment Programme). Commonly is discussed about NFI (National Forest Inventory) cycles 1 to 4. The fourth round (NFI4) was carried out between 2006 and The purpose of national forest inventory is to provide information on forest and tree resources and their long term changes on national and provincial level Currently, government of Vietnam is implementing a major exercise in form of National Forest Inventory and Statistics Programme, carried out between 2011 and Within this period, government funds used previously for NFI cycles are used to finance NFI & Statistics Programme. The purpose of this programme is to develop forest distribution maps and statistical data of forest resources at local level (province, district, community, village) down to individual compartments (forest stands) The focus of the programme is to provide reliable baseline information for operational, management planning purposes and further annual updates by FPD (Forest Protection Department) in communal level, to be aggregated to national level statistics NFA project is supporting this programme by developing computerized tools for land use and forest type mapping utilizing remote sensing data and advanced IT-solutions NFI Cycle UN REDD Phase II FORMIS Phase II NFI Cycle NFI Cycle NFI Cycle Development of change detection and carbon monitoring systems (supported by NFA) and benefit distribution mechanism Development of centralized forestry database and information sharing system Learns from past experiences in Vietnam and best practices from abroad Develops methodology for future NFIMAP to provide data on forest resources on national and provincial level Supports NFI & Statistics Programme NFIMAP ? NFA Project NFI & Statistics NFIMAP Develops forest distribution maps and statistical data on forest resources on local levels (province, district, community, village) Map production and data analyses supported by NFA Picture 4: NFA project relationship with other forestry projects 8 P a g e

9 NFA project is designing improved national forest inventory system to be implemented in Vietnam during the next national forest inventory round between 2016 and NFA project is not carrying out any large scale national forest inventory excluding some pilot tests. Nationwide inventory will follow in the next phase after the development of the methodology and implementation decision of Vietnamese Government. NFA project is getting ready for next NFIMAP round by: Developing data collection, input, verification, calculation, analyses and dissemination tools Hardware and software solutions for whole NFIMAP process Strengthening institutional and human resources through training of all personnel involved Discussions have been initiated to unify the methodologies and sampling design of NFIMAP and NFI & Statistics Programme. If field sampling of both programmes can be unified, data collected could be utilized by both programmes. NFA has very important role in developing methods and tools within the FAO Finland Forestry Programme to serve FAO to be used in other member countries. The role of FORMIS project related to NFA project is to serve as a data warehouse and information sharing channel of future NFIMAP results. According to current understanding, the raw data management, calculation and analyses of future NFI cycle s data is to be done in FIPI s servers. Aggregated data will be linked to FORMIS platform in form of national and provincial level statistics and maps on forest resources. UN REDD project phase 2 is developing change detection, carbon monitoring and benefit distributions mechanisms for REDD initiative. NFA project plans to integrate annual, national level change detection using medium size resolution satellite imagery to be part of NFIMAP implementation. This data would serve directly REDD s annual change detection and reporting need on national and provincial level. Additionally, NFA project has already developed very advanced mapping tools for high resolution SPOT-5 satellite imagery as a contribution to NFI & S Programme. These techniques could be very useful for UN REDD hot spot analyses as well. 4. Improved NFIMAP design for Vietnam National Forest Inventory, Monitoring and Assessment Programme (NFIMAP) can answer to these questions and demands: Forest coverage and their annual changes in a reliable way and with known error estimates. Total volume, biomass and carbon sequestered into ecosystem. This is a compulsory part of REDD reporting. Annual growth of forests is needed to estimate maximum annual allowable cut. The basic principle in sustainable forestry is that forest resources are not utilized more than annual growth. Impact of climate changes to growth. These long term trends can be evaluated only after repeated measurements of NFI rounds and permanent sample plots. Natural regeneration of forests, tree species proportions and their annual changes (possible losses in biodiversity) can be found out only with repeated measurements over fixed period of time. NFIMAP based on systematic, nationwide sampling is a cost efficient way to cover necessary information needs and fulfill international reporting requirements. All this information is needed for 9 P a g e

10 1000 m GCP/GLO/194/MUL/(FIN)-VN global FAO FRA and REDD reporting. Aggregated statistics of forest coverage %, previously summarized by Forest Protection Department are not reliable enough for international reporting and their reliability is not known. National Forest Inventory and Statistics Programme cannot either answer to these questions, because growth and carbon are not measured. According to current knowledge, National Forest Inventory & Statistics Programme is going to be one time large scale exercise between 2011 and 2015, to be continued later on with annual update of changes at local level. 4.1 Analyzing NFI cycles I IV sampling design and data collection Main deficiencies in previous design were the following. Measurement of highly correlated neighboring plots. o In statistical point of view, measurement of highly correlated neighboring plot does not make sense. o Variation is good thing in forest inventory. Sampling should be designed to maximize variation in the sample. All trees over 6 cm were measured, large number of small trees were measured representing small part of volume o Two thirds (2/3) of the time in field was used for measuring small size trees representing less than one third of the volume (1/3) Rectangular plot measurement in the field is difficult due to challenging terrain o Rectangular plots (L-shape lines of 40 sub-plots) neighboring each other with no gap between plots is difficult to identify in the field in correct location using map, compass and measuring tape only. In mountainous areas measurement can be even impossible. Costly and time taking implementation in the field o Historical data reveals that in the past one month was used to measure one L-shape plot with 40 sub-plots. Plots were established only in forested areas o No reliable estimates on land use classes or their changes o No information on trees outside forest Longitude 4 IIA 9.5 I IIIA IIB IC 5.8 N 5 IIIB IIIA IVB 8.6 Latitude Line for forest stand boundary definition 8 NN IIIB m Picture 5: NFI cycle 4 sampling design 10 P a g e

11 4.2 Development of improved field measurements and testing in the field Nested circular plots are widely used in national forest inventories worldwide for improved efficiency. In the new design proposed for NFIMAP field sample plot trees of different sizes would be measured from different radiuses: Measure all trees having DBH 6 cm within the circle with R = 5.64m (100 m 2 ) Measure all trees having DBH > 20 cm within the circle with R = 12.62m (500 m 2 ) Measure all trees having DBH > 40 cm within the circle with R = 17.84m (1000 m 2 ) Over 90 % of Bac Kan field test participants confirmed that nested circular plot is easier and faster to measure in the field compared to rectangular plot. Circular plots are often criticized, that they are difficult to establish and measure in hilly areas, because of slope correction. Fortunately, improved measuring tools like Vertex and TruPulse have built-in slope correction so that correct distance is easy to define. Both Vertex and TruPulse can be used for distance and tree height measurements. Vertex is based on ultrasound and TruPulse in based on laser m 12.62m 5.64m 1m Picture 6: Nested circular sample plot Picture 7: Vertex above, TruPulse below A study was carried out with NFI 4 data from Bac Kan province to analyze the impact of nested circular plots into number of trees measured. In NFI 4, all trees over 6 cm were measured. Large number of small trees was measured, even they represent small portion of volume. In this particular test, plantations were excluded to imitate more the conditions and diameter distribution of native forests. Total number of trees was When sample was taken from NFI 4 data using nested circular plots with radiuses 6, 12 and 15 meters, sample represents better volume distribution and the total number of trees was The total number of trees to be measured came down to one third. 11 P a g e

12 Picture 8: The percentage of trees and volume by diameter classes in NFI 4. For example, diameter classes 7, 9 and 11 represent approximately 48 % of all tree measured, but they represent only 10 % of total volume. Picture 9: The percentage of trees and volume by diameter classes in nested circular sample taken from NFI 4 data. For example, diameter classes 7, 9 and 11 represent approximately 31 % of all tree measured, but they represent 10 % of total volume. In overall, the sampling ratio and volume by diameter classes are more representative. The ratio of bigger trees measured is higher. 12 P a g e

13 Another major deficiency in NFI 4 sample plot design was the highly correlated neighboring sample plots. It was easy to calculate volume for each plot and compare the autocorrelation between plots by distance. Analyses were carried out with datasets from Bac Kan and Ha Tinh provinces. Bac Kan correlograms are presented below. As a conclusion is understood, distance between plots should be 150 meters or more to avoid volume autocorrelation of neighboring plots. Picture 10: The correlation of plots volume and distance between plots. Neigboring plots are highly correlated, plot volume correlation being 0.6. When distance between plots increases to 150 meters and more; correlation disappears. Picture 11: The correlation of land use, forested non forested land. Correlation reduced and stabilizes after 400 meters. 13 P a g e

14 4.3 Nationwide sampling design To be able to identify the optimum nationwide sampling design, some initial decisions must be made. What should the level of reporting and data analyses? Typically in national forest inventory, results are calculated and maps and statistics are developed for national and provincial level. Targeted accuracy of NFIMAP in Vietnam is: Combined error of m3/ha and forest cover % is no more than 10 % in provincial level Combined error of m3/ha and forest cover % is no more than 1 % in national level Different sampling designs and their expected accuracy were tested utilizing volume and land use maps of Bac Kan with 1000 simulation rounds for each cluster design. The main steps in simulation process were: 1. Utilize data (plot measurement data and satellite images) of Bac Kan province 2. Create the volume map & land cover map 3. Choose a sampling design to be tested 4. Generate the location of the systematic grid of sample plots randomly, calculate the forest coverage, forest area, mean volume and total volume 5. Repeat the above step times, estimate the empirical and theoretical errors of total volume 6. Repeat from Step 3 for other sampling designs 7. Analyze the results to select the best one Picture 12: Volume map prepared with knn-methodology for Bac Kan on the left, land use and forest type map on the right, prepared with ecognition software. The following elements were analyzed: shape of cluster, number of plots in cluster, distance between plots inside cluster and distance between clusters. 14 P a g e

15 Theoretical error (%) GCP/GLO/194/MUL/(FIN)-VN The first topic to analyze was the overall shape of cluster. Different cluster shapes are used in different part of world, line, L-shape and rectangular clusters being the most common ones. Line L-shape Rectangular Picture 13: The most common cluster types used, line, L-shape and rectangular. Line form has the least auto-correlation between plots but is the most difficult to implement in the field, because after finishing the measurement of last plot, there is a long way to walk back to the starting point. Rectangular form is easiest to implement, but has the highest auto-correlation between plots Line L-shape Rectangular Plot distance (m) Picture 14. Theoretical errors of mean volume The distance between clusters was fixed to be 8 km. Errors (both empirical and theoretical) are calculated for total volume. Rectangular cluster shape has the worst empirical and theoretical errors. L-shape cluster ranks second but the differences with Line shape are very small and are not statistically significant with simulations. 15 P a g e

16 Empirical error (%) Theoretical error (%) Empirical error (%) Theoretical error (%) GCP/GLO/194/MUL/(FIN)-VN The L-shape form lies between these two forms (line and rectangular cluster) and it was selected as overall cluster shape design being suitable for Vietnamese conditions. The next topic was to analyze, what would be the optimum number of plots in each cluster. For each design, the empirical errors are slightly smaller than the theoretical errors. Increasing the number of plots will reduce the errors in all designs. Increasing the number of plots from 7 to 9 and further only reduces the errors slightly The best numbers of plots are 5 or 7 (depends on the desired accuracy level) N = 1 N = 3 N = 5 N = 7 N = 9 N = Plot distance (m) Picture 15. The empirical and theoretical errors for L-shape cluster with different number of plots in cluster. The ideal distance between plots depends on the terrain and landscape, how much there really is variation in the population (forests) to be measured. Both NFI 4 correlogram analyses and volume map based simulation analyses confirm that distance between plots inside cluster should be at least 150 meters. Increasing the distance between plots will reduce the errors. However, increasing the distance between plots from 150 m to 200 m and further only reduces the errors slightly The best distance between plots should be 150 meters N = 1 N = 3 N = 5 N = 7 N = 9 N = Plot distance (m) D = 50 D = 100 D = 150 D = 200 D = Number of plots Picture 16. The empirical and theoretical errors for L-shape cluster with different number of plots and different distance between plots in cluster. The next step was to identify the ideal distance between clusters. The following distances between clusters were tested: 4, 8, 12, 16, 20 and 24 km. Result were compared with single plot cluster that in fact can express the highest accuracy, what can be received with systematic sampling grid and certain number of plots for given geographical area. Totally 180 different L-shape cluster designs were tested for their accuracy D = 50 D = 100 D = 150 D = 200 D = Number of plots 16 P a g e

17 Empirical error (%) Empirical error (%) GCP/GLO/194/MUL/(FIN)-VN The following conclusions can be made: 1) the graphs for theoretical errors are similar to those for empirical errors, 2) increasing the distance between clusters will increase the errors linearly, 3) when reducing the distance between clusters, the number of clusters increase quadratically, and 4) when increasing number of clusters from 77 (8km grid) to 308 (4km grid) errors only reduce slightly The best distance between clusters is 8km N = 1 N = 3 N = 5 N = 7 N = 9 N = km grid 12km grid 8km grid 4km grid N = 1 N = 3 N = 5 N = 7 N = 9 N = Cluster distance (km) Picture 17. The empirical errors for L-shape cluster with different number of plots (1-11 per cluster), comparing distance between clusters and total number of clusters needed Number of clusters 4.4 Accuracy and cost comparison of different sampling designs To be able to select the optimum sampling design, cost of implementation in the field has to be taken into consideration. In Table 1 below: Cost 1: doing survey in one cluster, Cost 2: moving between clusters. They are estimated based on expert judgment Total cost = Num. clusters (Cost 1 + Cost 2) Designs no. 2 and 3 have errors only slightly larger than those of NFIMAP design, but much less costly. Their total costs are, respectively, just 35% and 45% of the total cost of NFIMAP design If we want to keep the error level as NFIMAP design, then design no. 4 can be chosen with about half of total cost of NFIMAP design Table 1: Comparison of NFIMAP cycle 4 accuracy and cost with improved designs No Design type Plots per cluster Dist plot (m) Dist cluster (km) Empirical error (%) Theoret. error (%) Num. clusters Num. plots Cost 1 (teamday) Cost 2 (teamday) Total cost 1 NFIMAP L-shape L-shape L-shape In National Forest Inventory & Statistics Programme the latest idea has been to change the sampling system in province into single plot cluster desing. From statistical point of view we know, that a systematic single plot sampling grid will give the highest accuracy for a certain geographical area with a fixed number of sample plots. 17 P a g e

18 Having a look at Table 2, the following findings can be made: Design no. 2 (or 4) has the same empirical error with design no. 1 (or 3) Design no. 3 (or 6) has the same number of plots with design no. 1 (or 3) The single plot cluster design needs the least number of plots to reach a certain level of accuracy, but is not the most cost-effective design The errors of designs no. 1 and 4 are just about 1.5% higher than the errors of the best designs with the same number of plots Table 2: Comparison of improved NFIMAP accuracy and cost with single plot cluster design No Design type Plots per cluster Dist plot (m) Dist cluster (km) Empirical error (%) Theoret. error (%) Num. clusters Num. plots Cost 1 (teamday) Cost 2 (teamday) Total cost 1 L-shape Point 1 na Point 1 na L-shape Point 1 na Point 1 na As a final conclusion for samling design were made: The most effective sampling designs are L-shape clusters of plots, with the number of plots being 5 or 7, the distance between plots being 150m, the distance between clusters being 8 km. Both designs suggested have errors 0.5% - 1.0% higher than those of past NFIMAP cycle 4 design, but much less costly. The single plot cluster design needs the least number of plots to reach a certain level of accuracy, but is not the most cost-effective design. This finding is applicable for National Forest Inventory and Statistics Programme. 4.5 Total number of clusters and plots needed for nationwide NFIMAP implementation If a systematic sampling grid of clusters will be displayed throughout the country using 5 or 7 plots in each cluster, 150 meters being distance between plots and distance between clusters would be eight kilometers, the following total number of clusters and plots would be needed to cover whole Vietnam, see Table 3. Table 3. Number of clusters and plot in old and future NFIMAP Future NFIMAP Number of clusters Plots per cluster Number of plots 8 km grid everywhere, on all land uses km grid everywhere, on all land uses Old NFIMAP Number of clusters Plots per cluster Number of plots 8 km grid on forested land only P a g e

19 4.6 Comparison of NFI 4 and NFIMAP differences In the below tables 4 and 5 the main differences of NFIMAP cycle 4 and improved NFIMAP are summarized. Table 4. Main differences between NFIMAP cycle 4 and improved NFIMAP Item NFI-4 NFIMAP Coverage of sample Forested area only All land uses Plot (cluster) 40 sub-plots in L-shape 5 or 7 plots in L-shape Plot shape Rectangular 20 x 25 m totalling 500 m2 for each sub-plot Distance between plots 25 meters 150 meters Correlation between plots High Low Nested circular 100, 500 and 1000 m2 Ratio of trees measured 100 % (over 6 cm) 35 % (Bac Kan case study) Sub-plot demarcation in the field PSP (Permanent Sample Plot) demarcation in the field Socio-economic survey with FGM elements Trees outside forest measured? Dead wood measurement carried out? Concrete pole and map coordinates for L-shape corner only Concrete pole and map coordinates for L-shape corner only Limitations in data collection and analyses No No GPS coordinates for each sub-plot Plastic pipe inside ground and 3 reference points for each PSP-plot Household survey, methodology improved Yes, based on systematic sampling grid over all land uses Carbon calculations exist? No Yes, using models, litter and soil samples collected Data input, verification and validation Result calculations for national and provincial levels Thematic mapping using satellite images Custom made VB 6 tools, standalone computers only Data delivery by mail on CD-ROM Based on measured ground sample plots only Manual calculations No Yes OpenFORIS Collect tool Remote access via Internet Data storage directly on FIPI server or on mobile device Based on combined use of ground sample plots and satellite image interpretation OpenFORIS tools Yes 19 P a g e

20 5. The principles and expected outputs of improved NFIMAP programme The following principles should be followed in national forest inventories to ensure sufficient data quality. First of all, all plots are measured / classified. The level of assessment depends on accessibility: 1) land use and forest type classification based on satellite image only 2) remote visual assessment (when plot can be seen but cannot be accessed due to a difficult terrain) 3) on-site measurements. NFIMAP should be a continuous inventory, 20 % of clusters (every 5 th cluster) should be measured annually, and 1031 clusters in a year. By continuous inventory, FIPI and sub-fipi staff members' expertize and skills would not be lost Annual nationwide change detection of land use and forest coverage change from DMCI satellite imagery would be an integrated part of inventory Annual updates for REDD reporting, regardless their reporting interval would be available FAO FRA updates would be available in every 5 years. After first year of implementation, the NFI results would be obtained already. During the following 4 years, with annual updates the accuracy would improve every year, until the highest accuracy would be received after 5 years of implementation and field measurements. Consequently, during the following years annual updates would be received with highest accuracy level. All sample plots are established as permanent ones during the first 5 years of inventory. Net growth is verified based on re-measured permanent sample plots in five years intervals (net growth = growth removals). For example, clusters measured during 1 st year of implementation, would be re-measured during 6 th and 11 th year and then after every 5 years. Targeted maximum errors for mean volume per hectare are: 10 % in provincial level (after 5 years of implementation, around 5-6 % only) no more than 1 % in national level Transparency of the process and results is a must. The whole process can be verified by any 3 rd parties for international recognition and acceptance of results. 20 P a g e

21 5.1 Expected outputs of future NFIMAP programme For changes in land use and forest cover updates will be received annually both from field measurement and DMCI satellite image change detection analyses (please refer to chapter 5.2, why DMCI imagery is recommended). For growth and drain the first reliable estimates can be received after re-measured plots in age of 5 years after establishment. Nationwide and annual DMCI land use analyses is a way to verify and crosscheck how well field measurements and satellite image interpretation match with each other. DMCI change detection indicates areas where hotspot analyses with higher resolution images may be needed. Table 5. Expected outputs of future NFIMAP programme Output Area and percentage of land By land use classes (forest, agriculture, water ) By forest types By tree species groups Volume of Growing stock Biomass Sequestered carbon Both inside forest and outside of forests Volume of total drain Harvesting Natural losses (fire, damages, storms) Growth by Tree species Tree species group Forest types Biodiversity Health of forests Volume and value of Non-wood forest products Based on continuous Socio-Economic survey From field measurement Every year Every year Every year Every year Every year Every year After 5 years After 5 years After 5 years After 5 years After 5 years Every year Every year Every year From DMCI satellite images Every year Remarks Accuracy of field measurements increases until 5 years Accuracy of field measurements increases until 5 years After re-measured permanent sample plots (5 th year), then annual updates After re-measured permanent sample plots (5 th year), then annual updates Accuracy of field measurements increases until 5 years Accuracy of field measurements increases until 5 years 21 P a g e

22 5.2 Utilization of satellite images as part of NFIMAP Programme Question is raised, whether satellite images and image interpretation are needed as part of national forest inventory? In principal, results of national forest inventory could be calculated from measured field sample plots only. Satellite images can be used: 1) to extrapolate the results obtained from sample plots to the areas which were not measured in the field 2) to produce maps for strategically planning 3) to calculate forest statistics over different units of analysis 4) to detect periodical changes in land cover and 5) to cross check results with field measurements. Picture 18. Source: Tomppo et. al, 2008, utilization prospects of satellite imagery in land use mapping and planning. There are certain limitations for forest cover mapping in Vietnam. In Vietnam like in many tropical countries the cloud cover is more or less persistent. Topography (hills and hill shadows), and forest structure (ever green tropical forests) add more challenge for image interpretation work. Additionally, land use/land cover changes take place in increasing speed. Availability of the remote sensing data is limited. Generally, the costs for remote sensing data and field data collection are high. NFA project has analyzed the potential RS data sources, which could be utilized in national forest inventory. The data sources are: Spot, Landsat, DMCI, RapidEye and others. The experiences gathered by NFA project utilizing ecognition software and Spot 5 imagery are very encouraging. The main findings of ecognition development work are. It is possible to produce accurate land cover and forest types maps using object oriented image processing of SPOT 5 data. For example, the overall accuracy of land use map reached 93 % in Ha Tinh and the accuracy of forest type map reached 84 % in Ha Tinh. Segments classification strategy for forest cover mapping in Vietnam has been developed. In order to produce the reliable maps the 2.5 resolution pan sharpened multispectral images from Spot 5 should be segmented with the scale parameter The slope and aspect calculated from Digital Elevation Model allowing significantly improve the accuracy of segmentation and classification. Image classification should be implemented in 2 steps approach: «forest/non-forest», «forest types» due to the different grops of features used in classification 22 P a g e

23 Key features for classification are: 1. Topography 2. Texture 3. Spectral values Picture 19: Forest type classification map of Ha Tinh province. Overall accuracy was 84 % for forest type mapping. This technology is very suitable for National Forest Inventory and Statistics Programme, which is targeting to produce high accuracy land use and forest type maps for local level management planning purposes. For nationwide annual utilization as part of national forest inventory, SPOT 5 and many other RS data sources (for example RapidEye) have some limitations. The main limitations are: Huge number of images are needed to cover the whole country Large number of images will be very costly Coverage of whole country is not possible to get during one year. In most cases, several years are needed for nationwide more or less cloud free coverage Large number of images needs to be processed, rectified and calibrated/homogenized before interpretation of larger areas. With calibration, information from original image is always lost. Calibration and homogenization of large number of images together can actually reduce the accuracy of image analyses Images taken during different years and different seasons are difficult to calibrate Even the whole country could be covered; the created satellite image map is already outdated, because parts of the images are several years old. To be able to measure land use changes of larger areas with relatively short intervals of time, we need satellite imagery that: Covers large areas Is cheap Is easy to obtain Has multispectral bands including NIR (near infra-red) 23 P a g e

24 As such satellite image source has been identified DMCI imagery. The key features of this imagery are: Spatial resolution 22 m Image size: 650km x 1600km Indicative costs for the 6 month cloud free coverage of Vietnam in 2012: euro (USD 90,000) The biggest advantage is that single image covers huge area compared to Landsat or SPOT imageries. Only few cloud free images could cover whole Vietnam. DMCI imagery has been successfully used in many tropical countries to monitor land use changes. For example, it has been successfully used in Brazilian Amazons annually since Picture 20. The number of images needed when utilizing different satellite image sources to cover Brazilian Amazon. NFA project has also studied the suitability of DMCI imagery in land use mapping and land use changes detection. Results are good. As findings from Bac Kan province case study can be concluded: Overall accuracy of map was 0.89 Accuracy for forest / land cover class was 0.81 The accuracy of forest type mapping was lower than 0.1 DMCI imagery is suitable for land use classification and large scale change detection, not for forest type classification Example of Bac Kan province land cover map at the following page. 24 P a g e

25 Picture 21. The land cover map created for Bac Kan province utilizing DMCI imagery 5.3 Data input, verification, validation and result calculation FAO Forestry HQ has been contributing to Open Foris Initiative development during the past few years. The principles of this initiative / software tool kit are: Open freedom to modify and adapt to country needs without special permission Free software available free of charge Sustainable global community of users; avoids vendor lock-in and dependence on outside support Tested incorporates knowledge and experience of many countries and institutions Tailored FAO and partners working closely with countries to meet specific national requirements Package includes tools for forest inventory data input, data management, and forest inventory results calculation as well as tools for remote sensing data processing. FAO Forestry HQ gives technical support in configuration of tools for different end-users. FAO Forestry HQ has developed Open Foris Collect software package that is based on open source and it s utilization is free of charge. Software package is already in use in Tanzania, Zambia, Peru, Ecuador, Indonesia and Paraguay. It will be soon used in PNG, Bhutan, Mongolia, and all of EU 25 P a g e

26 countries (within LUCAS project). In Vietnam it has been tested and configured for initial sampling design in late Open Foris Collect is designed for forest inventory data input, checking, validation and logical checkups. It has predefined menus and selections for user specific attributes including tree species lists. Software can be installed in server and it can be accessed anywhere through internet. Picture 22. An example of Open Foris Collect user enterface for inventory data input in Vietnam is presented above. The advantages of the software include: server setup possibility, simultaneous access through internet for several end-users ensuring data integrity, easy configuration to any inventory following field forms in logical order, any language can be used, predefined menus minimizing typing errors, tree species list inside with auto fill function (start typing, get proposals, logical checkup configuration for any value (for example diameter, height, database created automatically, free of charge, and online support from FAO Forestry HQ. Open Foris Collect Mobile has the same basic functionality with server/pc version. It is used in field data loggers. Data synchronization with server is arranged via GPRS connection or plugin to computer. Prototype 2.0 is used in Cambodia, Ghana and Kenya. Piloting is scheduled for summer 2013 in Peru. The main advantages in using data loggers are getting rid of paper sheets and improvements in data quality. Future NFIMAP programme in Vietnam should use data loggers too. Configuration for selected field computer of PDA device may cost some 15,000-20,000 USD, because it is done by a private company, not by FAO Forestry HQ. 26 P a g e

27 Open Foris Calc is tool for results calculation. It utilizes databases created with Open Foris Collect. End users in each country can define volume and height equations to be used. Once configured, it is really easy and quick to calculate stratify and summarize results by any given or measured attribute in the inventory. First prototype exists in Tanzania. In Zanzibar and Ecuador software versions for results calculation will be available by the end of summer Vietnam wishes to initiate Open Foris Calc result calculation tool development during second half of Resources needed and estimated costs 1031 clusters would to be measured every year. Based on Bac Kan field tests at autumn 2012, two plots can be measured in a day. Four days are needed for each cluster including socio-economic survey if number of plot is 5 in each cluster. Total field working time would be maximum 4124 days. If each team works 100 days in the field every year, 41 field teams would be needed. Five days are needed for each cluster including socio-economic survey if number of plot is 7 in each cluster. Total field working time would be 5155 days. If the number of crews would be the same 41, then each team should spend 125 days in field every year. By increasing the number of plots in cluster from 5 to 7 (40 % more plots measured), would increase field implementation costs by 25 % (from 4 days to 5 days). It should be kept in mind that forest coverage is estimated to be around 40 % in Vietnam. Many plots can be classified from satellite image to be agricultural land, urban area and water. For those plots field measurements are not needed. Table 6. Estimated field working time needed for continuous annual inventory. Work item Working days needed (5 plots/cluster) Working days needed (7 plots/cluster) Admin formalities Plot measurements (2 plots oper day) SE household survey Total time needed per cluster (days) Total number of days needed in a year The annual running costs are estimated for scenario, where number of plots per cluster is 5, continuous inventory is carried out and 20 % of clusters were measured every year. The number of field crews would be 41 and each of them would spend 100 days in field measurements every year. The annual running cost would be around USD 970,000 on a condition that 31 teams out of 41 needs to rent vehicle for whole field inventory period in each year. If sample grid with 7 plots in each cluster would be selected, the annual implementation cost would increase with 25 % from USD 969,000 to USD 1,220, P a g e

28 In the cost estimate is not taken into consideration of working time of FIPI and sub-fipi staff members outside the actual field inventory period. They are employed by government in any case and the calculation and analyses of the inventory results would be part of their normal duty. Table 7. Estimated annual running costs of continuous NFIMAP programme. Annual running costs Unit cost Units Total cost 5 persons field crew salaries in a day Field crew daily allowances, 4 persons Transportation, fuel cost per day (100 km driving) Nationwide coverage of DMC imagery Collection of field reference points for DMC Vehicle renting cost, 31 cars, 138 months Subtotal Management, insurances, micellaneous costs 15 % Total annual running costs In addition to annual running cost there would be on time costs to equip the 41 inventory teams with up to date measuring tools and equipment. This one-time cost would be USD 410,000, followed by some minor maintenance cost annually. Table 9. Estimated annual running costs of continuous NFIMAP programme. Other costs (one time cost) Unit cost Units Total cost PDA devices, field data logger Inventory tool set (TruPulse, Vertex, GPS etc.) One time costs total Proposal for NFIMAP national framework According to PM s decision from June 2012, there will be one National Forest Inventory, Assessment and Monitoring programme after 2015 (NFIMAP). NFA would be the national component of the NFIMAP Programme answering to FAO FRA and national level REDD reporting requirements. NFI & Statistics would be the provincial component of the NFIMAP Programme targeting to produce accurate maps and forestry statistics in local level to be further updated annually by FPD and FIPI. The key issue is: Both NFA and NFI & Statistics could be run simultaneously, if field sampling is unified in both programmes. This means that same systematic sampling grid should be used throughout the country. In field measurements, nested circular sample plots should be used for improved efficiency. If NFI & Statistics wants to have higher accuracy from smaller units and like district and communed, they can freely select more temporary sample plots inside each unit/geographical area like province, district or commune. 28 P a g e

29 NFA project can support NFI & Statistics by developing the following tools and trainings: Training on advanced forest inventory tools Land Use and Forest Type Mapping using ecognition software and FAO Open Foris RS tools Volume mapping tool utilizing knn-approach in satellite image and field sample plot data simultaneous interpretation Introduction of Open Foris Collect and Open Foris Calc tools. They could be utilized by NFI & Statistics Programme as well. Development of tree species and coding lists could be utilized by NFI & S programme. NFIMAP = National Forest Inventory, Monitoring and Assessment Programme 17.84m Unified field data NFA Analyses National level results 12.62m 5.64m 1m NFI & Statistics Analyses Provincial level results 27 Picture 23: Proposed NFIMAP Programme framework for Vietnam. Both national and provincial activities could be run simultaneously using unified field measurement. NFI & Statistics Programme sampling design in pilots carried out in Bac Kan and Ha Tinh is not optimal, neither their statistical accuracy is properly understood. The latest plans to change sampling to systematic single plot cluster, is a step to right direction. NFA project has really profoundly analysed the sampling issue to optimise the accuracy and minimize the costs of inventory in national and provincial level. NFA project recommends National Forest Inventory and Statistics Programme to adopt systematic cluster sampling design with 8 km between clusters, 5 or 7 plots in cluster and 150 meters between plots in cluster. This would be the first step in unifying field sampling practises in NFI. It is understood, that National Forest Inventory and Statistics Programme should produce volume estimates for down to individual compartment level. Hence, the sampling should be intensified further. More plots and clusters would be needed. One possibility would be to add more temporary sample plots and clusters between initial 8 km grid. This topic requires more analyses. 29 P a g e