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Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
Remote sensing and mapping tool development of NFA Project in Vietnam
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Remote sensing and mapping tool development of NFA Project in Vietnam

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  • 1. Remote Sensing and Mapping Tool Development by NFA Project in Vietnam
  • 2. NFI Cycle 1 UN REDD Phase II FORMIS Phase II1990-1995 2013-2017 2013-2017 • Development of changeNFI Cycle 2 detection and carbon • Development of centralized forestry1996-2000 monitoring systems database and (supported by NFA) and information sharing benefit distribution system mechanismNFI Cycle 32001-2005 • 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 levelNFI Cycle 4 • Supports NFI & Statistics Programme2006-2010 NFI Cycle 5 NFA Project NFI & Statistics2014-2016 ? 2011-2014 2011-2015NFI Cycle 5 • Develops forest distribution maps and statistical data on forest resources on local levels (province, district, community, village)2016-2020 • Map production and data analyses supported by NFA 2
  • 3. RS AND MAPPING TOOL DEVELOPMENTLand 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 requirementsWith 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 detectionWith 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) 3
  • 4. 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
  • 5. LAND USE AND FOREST TYPE MAPPING, MAIN STEPS 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 assesment14.11.2011 5
  • 6. EXAMPLE HA TINH PROVINCE, LAND USE CLASSIFICATION USING SPOT-5Land cover class Number of plots Training set Test setAgriculture 38 19 19Bare 18 9 9Forest 125 63 62Residential 47 24 23Water 14 7 7Total 242 122 120 • 242 plots, combined in 5 general land cover classes, checked in the field • 35525 segments were classified using 122 sample plots 6
  • 7. HA TINH LAND USE MAP (ECOGNITION) 7
  • 8. ACCURACY OF HA TINH LAND USE MAPUser Reference Class Forest Agriculture Water Bare SumForest 126833 3267 0 0 130100Agriculture 2944 15240 0 0 18184Water 0 265 3638 0 3903Bare 2414 0 0 15919 18333unclassified 2491 0 0 0 2491Sum 134682 18772 3638 15919Producer 0,94 0,81 1 1User 0,97 0,84 0,93 0,87Overall Accuracy 0,93 • Object level accuracy using eCognition software • Iterative process based on conflict / confusion matrix 8
  • 9. EXAMPLE HA TINH PROVINCE, FOREST TYPE CLASSIFICATION 9
  • 10. ACCURACY OF HA TINH FOREST TYPE CLASSIFICATION Class Total Correct Mixed % of correctForest garden 5 4 1 80.00Mixed timber and bamboo 5 4 1 80.00Natural evergreen broad leaved forest (<100m3/ha) 6 5 1 83.33Natural evergreen broad leaved forest (>200m3/ha) 5 5 0 100.00Natural evergreen broad leaved forest (100-200m3/* 8 7 1 87.50Natural regrowth 10 8 2 80.00Plantation 23 19 4 82.61Total 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 10
  • 11. LAND USE CLASSIFICATION USING DMCI IMAGERY IN BAC KAN PROVINCE • 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 11
  • 12. CONCLUSIONS OF WORK DONE• 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 12
  • 13. VOLUME MAP CREATION USING METLATOOLS, KNN APPROACH, MAIN STEPS Field sample SPOT 5 images plots Detecting clouds Checking raw data and shadows Volume calculation Merging and for field sample homogenizing plots imagesTest set: Plots Training set: Plots for Selecting bands for for accuracy volume map making volume map assessment development creation Accuracy Volume assessment Map 13
  • 14. FIELD SAMPLE PLOT DATA USED IN BACKAN VOLUME MAP CREATION 621 sample plots 2011-2012 from: - NFI & Statistics programme - NFA collected during Bac Kan pilot inventory 14
  • 15. SPOT-5 IMAGE PREPROCESSING Calibrated image: 4 multispectral bands 3 natural color bands 15 1 panchromatic band
  • 16. BAC KAN PROVINCE VOLUME MAP USING 7 NEAREST NEIGHBOURS - 621 plots: 500 for making volume map, 121 plots for accuracy assessment - Iterations: 10 times  RMSE: +/- 65 m3 in pixel level14.11.2011 16
  • 17. 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 17
  • 18. THANK YOU! Contact info: tani.hoyhtya@fao.org, tel. +84 (0)98242814714.11.2011 18

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