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
Plantations – one of deforestation reasons
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
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?
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
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
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
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.
Visual identification
Example of visual identification plantations Landsat 8 ETM+,combination of bands 6-5-4, Brazil.
ArcGIS 10.1
• 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
• 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
Result: vector layers of plantations 7 countries for now: Malaysia,Indonesia,Cambodia,
Brazil, Colombia, Peru, Liberia
In process result: samples catalogue
• ArcGIS online• GBIF• Panoramio
In process result: field data to share
• 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
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
THANK YOU! QUESTIONS?
[email protected] Jane Yolkina (mapping team)[email protected] Dmitry Aksenov (executive director)
Transparent World