Tree plantations mapping using Landsat imagery · Tree plantation definition. Classification. By...
Transcript of Tree plantations mapping using Landsat imagery · Tree plantation definition. Classification. By...
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Tree plantations mapping using Landsat imagery
Jane ElkinaJunior GIS team member, Transparent World
Russia
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Plantations – one of deforestation reasons
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Approach and expected resultsDevelop
plantations identifying and
mapping methodology
Mapping (vector layers)
Verifying maps (local
experts review, field
works)
Analyze and
improve mapping methods
Problem statement AS-IS
and TO-BE
Current status
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Methodology and challengesWhat is “tree plantation”?How to classify them?How to identify them using Landsat?How to recognize species of plantation? What sources are open to use?
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Tree plantation criteria: trees artificially planted for profit above 5-7 meters, the crown density about 10%quick rotation (5-10-30 years)
Tree plantation definition
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Classification
By groups/use (Timber, Rubber, Palms…)
By species (Eucalyptus, Hevea,Oil palm…)
By size (Large, Middle, Garden-like…)
Hevea plantation, Brazil. Landsat 8, combination of bands 6-5-4.
1:40000
>=100 ha
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Identification based on indicators:
Treecover, gain and loss layer Relatively long (comparing to croplands) rotation period:
10-30 years and more Lack of grass and forest fires over a long period of time The color (the brightness in satellite data spectral channels) Texture and structure The shape and the sharpness
of boundaries Roads, populated areas
How to identify plantations - methodology
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Manual identification
Examples of visual identification plantations: time comparison with croplands and Tree Gain-Loss layers. Landsat 8 ETM+,combination of bands 6-5-4, Brazil.
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Visual identification
Example of visual identification plantations Landsat 8 ETM+,combination of bands 6-5-4, Brazil.
ArcGIS 10.1
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• ERDAS or ScanEx Image Processor classification
Semi-automatic identification
FOREST
PLANTATION
CLEARING
1)Download Landsat image, composite bands, make masks
2) Collect samples of different area types
3) Create and train a neural network
4) Image classification using trained neural network
5)Raster to vector
6)Clearing, simplify geometry
7)Visual and topology check
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• Landsat http://glovis.usgs.gov/• High resolution images WMS, OpenLandscapeProject• Global Forest Watch Landcover
http://earthenginepartners.appspot.com/science-2013-global-forest
• General countries boundaries• Protected area boundaries http://intactforests.org/• Field points• Other: panoramio.com, wikimapia.org, fao.org
Sources
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Result: vector layers of plantations 7 countries for now: Malaysia,Indonesia,Cambodia,
Brazil, Colombia, Peru, Liberia
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In process result: samples catalogue
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• ArcGIS online• GBIF• Panoramio
In process result: field data to share
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• We need more automatic and semi-automatic methods and understanding of spectral characteristics
• Explore object oriented classification
• We need to use Geodatabase, use common rules for filling the attribute tables and use GDB topology rules
• Rethink our classification
• Crowdsourcing?
What we have learned
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q1 q2 q3 q4
2014 2015
Methodic completed
WRI results checking
q1 q2 q3 q4
Start: Indonesia, Congo Basin countries, Liberia, Colombia, Brazil, Peru
Data sharing (field data, patterns)
Start*: Equatorial (central) Africa –Congo, Cameroon, Nigeria, Zambia and Southeast Asia –Laos, Vietnam, Birma, India
Catalogue as a tool for distributed team
Roadmap
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THANK YOU! QUESTIONS?
[email protected] Jane Yolkina (mapping team)[email protected] Dmitry Aksenov (executive director)
Transparent World