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Forest loss year

Dataset Creator: Matthew Hansen
Publication Year: 2019

Data in this layer were generated using multi-spectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+) sensor. The clear surface observations from over 600,000 images were analysed using Google Earth Engine, a cloud platform for earth observation and data analysis, to determine per pixel tree cover using a supervised learning algor ...

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DOI: https://doi.org/10.1126/science.1244693

Big DataRemote SensingForest lossGoogleAIMachine LearningGlobal Change

Title:

Forest loss year

Publication Year:

2019

Creator

Name:

Matthew Hansen

E-mail: mhansen@umd.edu
Organisation:

University of Maryland

Contact

Name:

Matthew Hansen

E-mail: mhansen@umd.edu
Organisation:

University of Maryland

Essential Biodiversity Variables (EBVs)

EBV class

Ecosystem structure

EBV name

Ecosystem distribution

Spatial domain

Spatial extent:

Global

Spatial resolution:

1000 meter

Spatial accuracy:

1000 meter

Temporal domain

Temporal resolution:

Yearly

Temporal extent:

From 2001-01-01 to 2018-01-01

Biological entity

Entity type:

Ecosystem Types

Environmental domain

Realm:

Terrestrial

Metric

Name:

To be added soon

Description:

To be added soon


Scenario

No scenario provided

Description:
Keywords: Big DataRemote SensingForest lossGoogleAIMachine LearningGlobal Change
User comments:
profile-image Jul 2020
Christian Langer

This is outstanding. Very useful to detect forest change around the world!

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