B E E O D A

BEEODA: a suite of open-source software and

educational materials for processing Earth

Observation data

Pontus Olofsson, BU & SilvaCarbon

Eric Bullock, Boston University (BU)

Curtis Woodcock, BU, NASA, USGS, GOFC-GOLD

Chris Holden, Boston University

GOFC-GOLD Workshop

Cote d’Ivoire, February 8th 2017

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Why are we doing this?

▪ For our own research and teaching responsibilities! And for

capacity building initiatives (SilvaCarbon, GFOI, GOFC/GOLD)

▪ Remote sensing data quality/quantity and analyses advancing

rapidly -- proprietary software lagging and expensive -- open

source solution are free and dynamic and powerful!

▪ Lesson learned from teaching CP workshops: software to take

home and that are in line with new advancements needed

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CAPACITY BUILDING

Capacity building needs in tropical

countries; SilvaCarbon, NASA,

FAO, etc.

SOFTWARE

BEEODA: free open-source

software and educational materials

for EO analysis

EARTH OBS. DATA

Vast Landsat archive, lots of new

imagery recently added through

LGAC; + high resolution data

Remote sensing advancements:

time series analysis, OBIA,

statistical inference, etc.

ADVANCEMENTS

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What is BEEODA?

▪ BEEODA: Boston Education in Earth Observation Data Analysis

▪ Collection of: existing open-source tools +

our own implementations +

educational materials

▪ Philosophy: “try to use as much existing open source tools as

possible and only implement the missing pieces”

▪ Educational material divided into modules in line with

advancements in RS analyses such as time series analysis, OBIA

and accuracy/area estimation

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Technical aspects

▪ Runs as a virtual machine in Windows, OS X, Solaris and Linux

platforms

▪ Oracle VM VirtualBox (freeware) is the only software required

▪ Can also be run from a USB stick with no additional software

needed

▪ In addition to homemade tools, VM includes QGIS, GDAL,

Orfeo, R, Python, Git in a Linux Ubuntu operating system

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BEEODA modules

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Time series analysis module

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▪ One of numerous different approaches for multi-temporal change

detection using Landsat data

▪ Algorithm uses all spectral bands to predict future observations and

detect changes in observed data

▪ Model coefficients for all bands can be used for land cover

classification

▪ Land cover maps ‘synthetic’ images can be created for any date,

and land change maps for any time interval in the study period

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CCDC/YATSM

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Time series analysis

▪ CCDC implemented

Used by

▪ USGS: Land Cover Monitoring, Assessment, and Projection

(LCMAP; CCDC globally operational)

▪ IDEAM, Colombia: testing in national forest monitoring system

(SilvaCarbon Research and NASA CMS 2016)

▪ Various research projects (NASA Terra/Aqua, NASA CMS,

NASA LCLUC, NASA IDS, USGS Landsat Science Team)

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Land Change Monitoring, Assessment, and Projection (LCMAP)

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Estimated loss of secondary forest

Estimated primary forest Estimated conversion of primary forest to pasture

Estimated conversion of primary

forest to secondary forest

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Outputs:

Predicted, cloud-free images

(Any date)

Continuous Change Detection and Classification (CCDC)

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Continuous Change Detection and Classification (CCDC)

Outputs:

Land-cover classification

(Any date)

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Outputs:

Change maps

(Any time interval)

Continuous Change Detection and Classification (CCDC)

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Estimation module

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Estimation of activity data

▪ IPCC criteria: (i) no bias and (ii) quantify uncertainty to reduce

~ This means that you need to ~

1. Construct unbiased estimators of area (and not count pixels

in maps!)

2. Construct confidence intervals of area estimates

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BEEODA support for:

▪ Estimation activity data compliant with IPCC and GFOI MGD

▪ BEEODA aims at providing tools that allow practitioners to make

practical use these documents!

Sampling designs supported:

▪ Systematic and random sampling

▪ Stratified sampling

▪ Two-stage cluster sampling

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BEEODA, estimation approaches:

▪ Analysis by stratified estimation (Cochran, 1953) following

Olofsson et al. (2013)

▪ Analysis of two-stage cluster sample using ratio estimators

Särndal et al. (1992) following a few recent research papers

▪ Analysis of sample when map and stratification differs using

indicator functions following Stehman (2013) (e.g. construction of

annual estimators using one sample)

▪ Implementation underway of model-assisted regression

estimator (Särndal et al., 1992) as used by McRoberts (2011)

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Two-Stage Cluster Sampling

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Object based image analysis module

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Object based image analysis

▪ Popular mapping used methodology but proprietary software for OBIA

often prohibitively expensive; BEEODA contains open source

solution

▪ Two steps: Image segmentation and object classification

▪ Orfeo Toolbox: Mean-shift clustering and support vector machine

classification, in QGIS

▪ Still a work in progress

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High-resolution imagery

1.

2.

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Landsat

1.

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3.

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Additional Modules

▪ Basic image processing

▪ Change detection

▪ Direct classification, not post-classification

▪ Using a global map

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Status and future directions

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Advantages

▪ Fully open-source, available on GitHub

▪ Developed by researchers -- in line with advancements

▪ Customizable (add your own or other open-source tools)

▪ Dependencies solved (major issue in Windows)

▪ One single software installation (or none if booting from USB)

▪ Runs offline on basically any computer

1/12/2016

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Current status

▪ Working on new and existing modules

▪ Used in workshops and university courses and by researchers

▪ Not really funded

▪ Currently maintained by BU researchers and grad students...

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BEEODA; teaching

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• Five three-week hands-on workshops at USGS/BU

• Used proprietary software until recently – not ideal

• USAID/FCMC workshop in Peru on estimation using open-

source but no VM – better but issues

• START/GOFC/NASA workshop at BU Jul/Aug 2015 using

BEEODA – most successful solution to date

• Used in BU remote sensing courses since fall 2015

• Ready for hands-on workshops in countries

-START/GOFC/NASA, Bangkok Oct 2016, 40 people

-MINAM, Lima Peru Oct 2016, 8 people

B E E O D A

Email: bullocke@bu.edu

olofsson@bu.edu

Web: http://beeoda.org

GitHub: http://github.com/beeoda

http://github.com/ceholden

http://github.com/parevalo

http://github.com/bullocke

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Example: Using a Global Map

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Comments

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Comments

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Comments from participants (Jul/Aug 2015)

▪ “I learnt processing of data using open source software [...] which makes it easily

transferable to my networks and other developing country researchers due to the

free access [...]” Mercy South Africa

▪ “A crucial advantage of the training was that we performed all these advanced

methods using open-source software [...] By this training, I not only acquired a lot of

data and processing techniques and tools which will be shared at my home

institutions, I can rather be considered as a candidate for any land cover change

detection and estimation analysis. Furthermore, I can contribute to a good

implementation of National REDD program and to the capacity building in my region.”

Maleki, Togo

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Comments from participants (Jul/Aug 2015) “One important limitation […] I had experienced was that commercial software are often

prohibitively expensive. Often there is a need of more than one software package for

completing the analysis procedures. Further, the installation and operation of those systems

are often operating system dependent and have many issues when running on even on

machines with different versions of windows. Here, […] significant add-ons was the open

source set of tools in the form of Virtual Machine provided to us. This has the potential to

address most of the issues with conventional software systems. This can be installed as an

independent operating systems and has a set of tools ranging from basic spreadsheets

program to advanced image processing. Further, the tools are open source, thus we can use,

adapt as per our requirement to solve diverse data analysis problems. And also all the

tutorials are provided […] with free access to anybody.

[In] my work, I have been volunteering a significant amount of time trying to help

students and other fellow researchers with their data analysis issues. In doing this one

important limitation was the software as for example I could demonstrate the data analysis

process in a machine with a licensed software but they could not try those on their own

machine. This was mainly due to the lack of licensed software and often the operation

system itself or component requirements within the operating systems. Now, I am confident

that access to as well as skills on using this tools will be very beneficial.” Shiva, Nepal

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Comments from participants (Jul/Aug 2015)

▪ “[The] two week data analysis exercise at Boston University is really excellent. I

really liked the neat planning and execution of training in terms of objectives like data

collection, classification and accuracy assessment. [...] The use of open source tools

such as QGIS, Orfeo toolbox, GDAL, Python and other in house developed tools by

Boston University team provided effective and ever improving solution for satellite

data analysis.” Suryakant , India

▪ We know for sure that many participants and their colleagues are currently using

BEEODA and at their home institutions.

▪ In short: very well received. This is also true for the BU students who have are

excited to have the whole system for analysis on their laptops – for completing labs

but also for future use.

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▪ QGIS to call GDAL library;

command-line optional

▪ GUI-based mainly but some

functions from command-line

▪ All tools run in a Virtual

Machine

▪ Open-source (CC BY-SA 4.0,

MIT and GNU GPL)

▪ Runs in Windows, OS X,

Linux

BEEODA

▪ “GDAL command-line

utilities”

▪ “Collection of prototype

command-line utilities”

▪ “Stand-alone programs and

scripts”

▪ Open-source (“GNU GPLv3

license”)

▪ Runs in Windows (CygWin),

OS X, Linux

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Quotes from UN-FAO (2013). Open Foris Geospatial Toolkit User Manual, Version 1.25.4

UN-FAO OFGT