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?

elkinaes@gmail.com Jane Yolkina (mapping team)picea2k@gmail.com Dmitry Aksenov (executive director)

Transparent World