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Remote Sensing and Mapping Tool Development by NFA
Project in Vietnam
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NFI Cycle 21996-2000
NFI Cycle 11990-1995
NFI Cycle 32001-2005
NFI Cycle 42006-2010
NFI & Statistics2011-2015
NFA Project 2011-2014
NFI Cycle 52016-2020
• Learns from past experiences in Vietnam and best practices from abroad
• Develops methodology for NFI Cycle 5 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
NFI Cycle 52014-2016 ?
RS AND MAPPING TOOL DEVELOPMENT
Land Use and Forest Type mapping and change detection:
• For NFA and future NFI cycles
• To support National Forest Inventory and Statistics Programme
• To support REDD+ MRV reporting requirements
With the following satellite imagery data:
• SPOT-5, high resolution, 10 meter resolution, 2.5 pan-chromatic for
local level forest type and land use mapping
• DMCI, medium resolution 22 m for large scale mapping and change
detection
With following software:
• eCognition (land use and forest type mapping)
• FAO OpenFORIS RS toolkit (land use and forest type mapping)
• METLA developed Open Source tools (volume map creation and
sampling simulations)
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WHAT HAS BEEN AND WILL BE DONE
• Developed rule sets for land use and forest type classification for
SPOT-5 and DMCI imageries using eCognition software
• NFA project plans to convert SPOT 5 and DMC satellite imagery
based land use and forest type classification tools developed with
eCognition to be part of Open Foris RS package to be used by any
parties, work is on-going
• NFA project has acquired two sets of DMC imageries from North-
East part of Vietnam from years 2010 and 2012. This dataset will be
used to develop land use and forest type change detection tools to
monitor nationwide changes though time, work on-going
• NFA plans to integrate annual / biannual change detection from
DMCI imagery to be part of NFI cycle 5
• Land use and forest type classification and change detection tools
could be utilized by REDD+ initiative 4
LAND USE AND FOREST TYPE MAPPING,
MAIN STEPS
14.11.2011 5
1. Dem preparation: to calculate the slope and aspect
2. Satellite image pre-processing
3. Field data collection
4. Field data division into training and test sets
5. Rule set development and image segmentation
6. Segments classification
7. Accuracy assesment
EXAMPLE HA TINH PROVINCE, LAND
USE CLASSIFICATION USING SPOT-5
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Land cover class Number of plots Training set Test set
Agriculture 38 19 19
Bare 18 9 9
Forest 125 63 62
Residential 47 24 23
Water 14 7 7
Total 242 122 120
• 242 plots, combined in 5 general land cover classes,
checked in the field
• 35525 segments were classified using 122 sample plots
HA TINH LAND USE MAP (ECOGNITION)
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ACCURACY OF HA TINH LAND USE MAP
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User \ Reference Class Forest Agriculture Water Bare Sum
Forest 126833 3267 0 0 130100
Agriculture 2944 15240 0 0 18184
Water 0 265 3638 0 3903
Bare 2414 0 0 15919 18333
unclassified 2491 0 0 0 2491
Sum 134682 18772 3638 15919
Producer 0,94 0,81 1 1
User 0,97 0,84 0,93 0,87
Overall Accuracy 0,93
• Object level accuracy using eCognition software
• Iterative process based on conflict / confusion matrix
EXAMPLE HA TINH PROVINCE, FOREST
TYPE CLASSIFICATION
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ACCURACY OF HA TINH FOREST TYPE
CLASSIFICATION
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Class Total Correct Mixed % of correct
Forest garden 5 4 1 80.00
Mixed timber and bamboo 5 4 1 80.00
Natural evergreen broad leaved forest (<100m3/ha) 6 5 1 83.33
Natural evergreen broad leaved forest (>200m3/ha) 5 5 0 100.00
Natural evergreen broad leaved forest (100-200m3/* 8 7 1 87.50
Natural regrowth 10 8 2 80.00
Plantation 23 19 4 82.61
Total 62 52 10 83.87
• Object level accuracy is higher compared to pixel level accuracy
• The use of DEM in the process doubles the accuracy
LAND USE CLASSIFICATION USING DMCI
IMAGERY IN BAC KAN PROVINCE
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• Overall accuracy was
0.89
• Accuracy for forest
cover class: 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
CONCLUSIONS OF WORK DONE
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• It is possible to produce accurate land cover and forest types
maps using object oriented image processing of SPOT 5 data
• The slope and aspect calculated from Digital Elevation Model
significantly improves the accuracy of segmentation and
classification (doubles the accuracy).
• Image classification should be implemented in 2 steps
approach: «forest/non-forest», «forest types» due to the
different groups of features used in classification
• Key features for classification: 1. Topography 2. Texture 3.
Spectral values
• Alternative data source for forest cover monitoring is DMCI
VOLUME MAP CREATION USING METLA
TOOLS, KNN APPROACH, MAIN STEPS
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Field sample
plots
SPOT 5 images
Detecting clouds
and shadows
Merging and
homogenizing
images
Selecting bands for
making volume map
creation
Checking raw data
Volume calculation
for field sample
plots
Test set: Plots
for accuracy
assessment
Training set: Plots for
volume map
development
Volume
Map
Accuracy
assessment
FIELD SAMPLE PLOT DATA USED IN
BACKAN VOLUME MAP CREATION
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621 sample plots
2011-2012 from:
- NFI & Statistics
programme
- NFA collected
during Bac Kan
pilot inventory
SPOT-5 IMAGE PREPROCESSING
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Calibrated image:
4 multispectral bands
3 natural color bands
1 panchromatic band
BAC KAN PROVINCE VOLUME MAP
USING 7 NEAREST NEIGHBOURS
14.11.2011 16
- 621 plots: 500 for
making volume
map, 121 plots for
accuracy
assessment
- Iterations: 10
times
RMSE: +/- 65
m3 in pixel level
POTENTIAL USE OF VOLUME MAP
• In sampling simulation and accuracy assessment using
tools developed by METLA
• Number of plots and clusters needed for desired accuracy
• Shape of cluster, to identify statistically most efficient cluster
design
• Distance between plots inside cluster
• Distance between clusters
• Used together with land use / forest type map to predict
• To be used by NFI & Statistics and UNREDD
programmes to predict volumes for smaller geographical
units and identify required sampling intensity
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