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

GCP/GLO/194/MUL/(FIN)-VN Overview of Improved NFIMAP Methodology in VN

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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 & 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


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


Volume and value of Non-wood forest products Based on continuous Socio-Economic survey

Every year



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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)


24 | P a g e

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.


25 | P a g e

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


26 | P a g e

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.


27 | P a g e

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


28 | P a g e

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


29 | P a g e

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

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