Sustainable Forest Management in Changing Climate · Finnish Forest Research Institute, METLA,...
Transcript of Sustainable Forest Management in Changing Climate · Finnish Forest Research Institute, METLA,...
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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
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Table of Contents
1. NFA project overview ...................................................................................................................... 4
1.1 Objectives...................................................................................................................................... 4
1.2 Activities ........................................................................................................................................ 4
1.3 Outcomes ...................................................................................................................................... 5
Partners ............................................................................................................................................... 5
2. NFI’s in international framework and FAO’s role in it .................................................................... 6
3. NFA project framework, linkage to other forestry programmes .................................................... 8
4. Improved NFIMAP design for Vietnam ........................................................................................... 9
4.1 Analyzing NFI cycles I – IV sampling design and data collection ................................................. 10
4.2 Development of improved field measurements and testing in the field ................................... 11
4.3 Nationwide sampling design ....................................................................................................... 14
4.4 Accuracy and cost comparison of different sampling designs .................................................... 17
4.5 Total number of clusters and plots needed for nationwide NFIMAP implementation .............. 18
4.6 Comparison of NFI 4 and NFIMAP differences ........................................................................... 19
5. The principles and expected outputs of improved NFIMAP programme ......................................... 20
5.1 Expected outputs of future NFIMAP programme ....................................................................... 21
5.2 Utilization of satellite images as part of NFIMAP Programme ................................................... 22
5.3 Data input, verification, validation and result calculation .......................................................... 25
5.4 Resources needed and estimated costs...................................................................................... 27
6. Proposal for NFIMAP national framework .................................................................................... 28
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Acronyms CC CART
Climate Change Classification and regression trees
DEM Digital Elevation Model DMC Disaster Management Constellation FAO Food and Agricultural Organization of The United Nations FAOR FIPI
FAO Representative (in member countries) Forest Inventory and Planning Institute
FLEGT FORMIS
Forest Law Enforcement, Governance and Trade Support Programme Development Of Management Information System For Forestry Sector
FRA Forest Resources Assessment GHG Green House Gas GIS Geographic Information Systems GO Governmental Organization GPS Global Positioning System IPCC KIA
Intergovernmental Panel on Climate Change Kappa coefficient of inter-rater agreement
MARD Ministry of Agriculture and Rural Development METLA Finnish Forest Research Institute MONRE Ministry of Natural Resources and Environment MRV NFA
Monitoring, Reporting and Verification National Forest Assessment (Project)
NFI National Forest Inventory NFMA NFIMAP NGO
National Forest Monitoring and Assessment National Forest Inventory, Monitoring and Assessment Programme Non-governmental Organization
NWFP Non-wood Forest Product NRSC National Remote Sensing Center PDA Personal Digital Assistant, mobile device PSP Permanent Sample Plot REDD Reducing Emissions from Deforestation and Forest
Degradation RS SFM
Remote Sensing Sustainable Forest Management
ToF Trees Outside of Forests UN UNFCCC USD UTM
United Nations United Nations Framework Convention on Climate Change United States Dollar Universal Transverse Mercator
VNFOREST Vietnam Administration of Forestry VN2000 Vietnamese Coordinate System
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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 2011. 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$3 3,2 million for 2011 – 2013, of which the Government of Finland through the FAO-
Finland Forestry Programme funded US$ 2.7 million and the contribution of Vietnam Government is
US$ 489,000.
1.1 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.
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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
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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.
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 1946. 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.
Forest land
Village-Local community
Global community
National Level
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
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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.
Picture 3. The role of FAO in capacity building and support to national programmes.
US$0.3–3M, 2-3 years
FAO Forestry Capacity development / FAO assistance
• 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 writing to fit agreed format of national reports
• Technically clear reports
• Help in triggering and stimulating national policy analysis
• Help design projects respond to country’s needs
Field Implementation
Data Processing ReportingPolicy
AnalysisDesign
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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
2010.
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 2016. 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
Picture 4: NFA project relationship with other forestry projects
NFI Cycle 21996-2000
NFI Cycle 11990-1995
NFI Cycle 32001-2005
NFI Cycle 42006-2010
NFI & Statistics2011-2015
NFA Project 2011-2014
NFIMAP2016-2020
• 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
• 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
UN REDD Phase II2013-2017
FORMIS Phase II2013-2017
• Development of change detection and carbon monitoring systems (supported by NFA) and benefit distribution mechanism
• Development of centralized forestry database and information sharing system
NFIMAP2014-2015 ?
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NFA project is designing improved national forest inventory system to be implemented in Vietnam
during the next national forest inventory round between 2016 and 2020. 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
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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
Picture 5: NFI cycle 4 sampling design
Line for forest stand boundary definition
1000 m
1 2 3 4 5 6 7 8 9
8 – NN
13.8
9 – IIIB
7.9
4 – IIA
9.5
2 – IIB
6.4 I – IIIA3
14.8
3 –
IC 5.8
7 – IVB
8.6 6 – IIIA2
20.4
5 – IIIB
13.8
Longitude
1000
m
Latitude
N
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17.84m
12.62m
5.64m
1m
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 m2)
Measure all trees having DBH > 20 cm within the circle with R = 12.62m (500 m2)
Measure all trees having DBH > 40 cm within the circle with R = 17.84m (1000 m2)
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.
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 18821.
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 6675. The total
number of trees to be measured came down to one third.
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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.
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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.
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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 1.000 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.
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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.
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.
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 1.000 simulations.
Rectangular
Line
L-shape
6.0
6.2
6.4
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
0 50 100 150 200 250
The
ore
tica
l e
rro
r (%
)
Plot distance (m)
Line
L-shape
Rectangular
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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).
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.
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.
4
5
6
7
8
9
10
11
12
0 50 100 150 200 250 300
Emp
iric
al e
rro
r (%
)
Plot distance (m)
N = 1
N = 3
N = 5
N = 7
N = 9
N = 11
4
5
6
7
8
9
10
11
12
0 50 100 150 200 250 300Th
eo
reti
cal
err
or (
%)
Plot distance (m)
N = 1
N = 3
N = 5
N = 7
N = 9
N = 11
4
5
6
7
8
9
10
11
12
0 1 2 3 4 5 6 7 8 9 10 11 12
The
ore
tica
l e
rro
r (%
)
Number of plots
D = 50
D = 100
D = 150
D = 200
D = 250
4
5
6
7
8
9
10
11
12
0 1 2 3 4 5 6 7 8 9 10 11 12
Emp
iric
al e
rro
r (%
)
Number of plots
D = 50
D = 100
D = 150
D = 200
D = 250
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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
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.
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
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.
0
5
10
15
20
25
30
35
0 4 8 12 16 20 24
Emp
iric
al e
rro
r (%
)
Cluster distance (km)
N = 1
N = 3
N = 5
N = 7
N = 9
N = 11
0
5
10
15
20
25
30
35
0 50 100 150 200 250 300 350
Emp
iric
al e
rro
r (%
)
Number of clusters
N = 1
N = 3
N = 5
N = 7
N = 9
N = 11
24km grid
12km grid
4km grid8km grid
NoDesign
type
Plots
per
cluster
Dist
plot
(m)
Dist
cluster
(km)
Empirical
error (%)
Theoret.
error (%)
Num.
clusters
Num.
plots
Cost 1
(team-
day)
Cost 2
(team-
day)
Total
cost
1 NFIMAP 20 50 8 4.90 5.93 77 1540 10 1 847
2 L-shape 5 150 8 5.97 7.03 77 385 3 1 308
3 L-shape 7 150 8 5.37 6.40 77 539 4 1 385
4 L-shape 9 150 8 4.89 6.01 77 693 5 1 462
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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
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
NoDesign
type
Plots
per
cluster
Dist
plot
(m)
Dist
cluster
(km)
Empirical
error (%)
Theoret.
error (%)
Num.
clusters
Num.
plots
Cost 1
(team-
day)
Cost 2
(team-
day)
Total
cost
1 L-shape 5 150 8.0 5.97 7.03 77 385 3 1 308
2 Point 1 na 4.7 5.97 6.48 216 216 1 1 432
3 Point 1 na 3.6 4.51 5.01 385 385 1 1 770
4 L-shape 7 150 8.0 5.37 6.40 77 539 4 1 385
5 Point 1 na 4.3 5.37 5.88 270 270 1 1 540
6 Point 1 na 3.0 3.64 4.14 539 539 1 1 1078
Future NFIMAP Number of clusters Plots per cluster Number of plots
8 km grid everywhere, on all land uses 5155 5 25775
8 km grid everywhere, on all land uses 5155 5 25775
Old NFIMAP Number of clusters Plots per cluster Number of plots
8 km grid on forested land only 2100 40 84000
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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 2016-2020
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
Nested circular 100, 500 and
1000 m2
Distance between plots 25 meters 150 meters
Correlation between plots High Low
Ratio of trees measured 100 % (over 6 cm) 35 % (Bac Kan case study)
Sub-plot demarcation in
the field
Concrete pole and map
coordinates for L-shape corner
only
GPS coordinates for each
sub-plot
PSP (Permanent Sample
Plot) demarcation in the
field
Concrete pole and map
coordinates for L-shape corner
only
Plastic pipe inside ground
and 3 reference points for
each PSP-plot
Socio-economic survey
with FGM elements
Limitations in data collection
and analyses
Household survey,
methodology improved
Trees outside forest
measured?
No Yes, based on systematic
sampling grid over all land
uses
Dead wood measurement
carried out?
No Yes
Carbon calculations exist? No Yes, using models, litter and
soil samples collected
Data input, verification and
validation
• Custom made VB 6 tools,
standalone computers only
• Data delivery by mail on
CD-ROM
• OpenFORIS Collect tool
• Remote access via
Internet
• Data storage directly on
FIPI server or on mobile
device
Result calculations for
national and provincial
levels
• Based on measured ground
sample plots only
• Manual calculations
• Based on combined use
of ground sample plots
and satellite image
interpretation
• OpenFORIS tools
Thematic mapping using
satellite images
No Yes
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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 5th 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 1st year of implementation, would be re-measured
during 6th and 11th 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 3rd parties
for international recognition and acceptance of results.
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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 From field measurement
From DMCI satellite images
Remarks
Area and percentage of land By land use classes (forest, agriculture, water…) By forest types By tree species groups
Every year Every year Every year
Every year
Accuracy of field measurements increases until 5 years
Volume of Growing stock Biomass Sequestered carbon Both inside forest and outside of forests
Every year Every year Every year
Accuracy of field measurements increases until 5 years
Volume of total drain Harvesting Natural losses (fire, damages, storms)
After 5 years After 5 years
After re-measured permanent sample plots (5
th year),
then annual updates
Growth by Tree species Tree species group Forest types
After 5 years After 5 years After 5 years
After re-measured permanent sample plots (5
th year),
then annual updates
Biodiversity Health of forests
Every year Every year
Accuracy of field measurements increases until 5 years
Volume and value of Non-wood forest products Based on continuous Socio-Economic survey
Every year
Accuracy of field measurements increases until 5 years
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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 30-50.
• 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
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• 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)
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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: 72 000 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 2005.
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.
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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
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countries (within LUCAS project). In Vietnam it has been tested and configured for initial sampling
design in late 2012.
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.
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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 2013. Vietnam wishes to initiate Open Foris Calc
result calculation tool development during second half of 2013.
5.4 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.
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 1,220,000.
Work item
Working days needed
(5 plots/cluster)
Working days needed
(7 plots/cluster)
Admin formalities 0.5 0.5
Plot measurements (2 plots oper day) 2.5 3.5
SE household survey 1.0 1.0
Total time needed per cluster (days) 4.0 5.0
Total number of days needed in a year 4124 5155
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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.
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.
6. 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.
Annual running costs Unit cost Units Total cost
5 persons field crew salaries in a day 53.49 4124 220593
Field crew daily allowances, 4 persons 60 4124 247440
Transportation, fuel cost per day (100 km driving) 18 4124 74232
Nationwide coverage of DMC imagery 90000 1 90000
Collection of field reference points for DMC 55 60 3300
Vehicle renting cost, 31 cars, 138 months 1500 138 207000
Subtotal 842565
Management, insurances, micellaneous costs 15 % 126385
Total annual running costs 968949
Other costs (one time cost) Unit cost Units Total cost
PDA devices, field data logger 2500 41 102500
Inventory tool set (TruPulse, Vertex, GPS etc.) 7500 41 307500
One time costs total 410000
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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.
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.
27
NFIMAP = National Forest Inventory, Monitoring and Assessment Programme
NFA
NFI & Statistics
Unified
field data17.84m
12.62m
5.64m
1m
Analyses
Analyses
National level
results
Provincial level results