12th Regional Workshop on Forest Monitoring GEO GFOI Early

Warning Systems for Deforestation Report January 19-23, 2015

INPE Headquarters, São José Dos Campos, Brazil

Meeting Objective:

The main objective of this GFOI workshop in INPE, Brazil is to showcase the methodologies of existing early

warning systems to the Americas SilvaCarbon countries: Ecuador, Colombia, Peru and Mexico. Systems such as

DETER (Deforestation Detection in Real Time) from INPE will be discussed and analyzed. One half day of this

workshop will focus solely on fire early warning systems and their important in detecting degradation. This

workshop is essential to illustrate the effectiveness of these systems as an articulated set of procedures through

which it collects and processes information about foreseeable threats, and prevent deforestation. Early warning

systems are vital to the forest conservation and this showcase will help Latin American countries to learn from these

systems and move closer to real-time monitoring.

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Contents

Welcome and Introductions .......................................................................................................................................... 3

SilvaCarbon Program and Global Forest Observation Initiative .................................................................................... 3

INPE’s Brazilian Amazon Forest Monitoring Program ................................................................................................... 4

INPE Assessment of Forest Degradation – DEGRAD and DETEX .................................................................................... 5

Operation Applications of Early Warning Systems- DETER-B ........................................................................................ 6

DETER-B Field Data .................................................................................................................................................... 7

Country Presentations ................................................................................................................................................... 8

Status of the MRV System and the Integration of Early Warning Systems for Deforestation in Peru ...................... 8

Status and Plans for Implementation of an Early Warning System – Colombia...................................................... 11

Early Warning Systems – Mexico ............................................................................................................................ 12

Status and Plans for Implementation of an Early Warning System – Ecuador ........................................................ 14

EWS Methodologies ..................................................................................................................................................... 17

Near Real-Time Mapping of Forest Disturbances ................................................................................................... 17

Two MODIS-Based Approaches for Monitoring Forest Change in Near Real-Time ................................................ 19

Continuous Monitoring of Forest Change in Near Real-Time with Data from the MODIS Sensors .................... 20

Near Real-Time Monitoring of Land Cover Disturbance by Fusion of MODIS and Landsat Data ....................... 21

ForWARN: A Cross-Cutting Forest Resource Management and Decision-Support System .................................... 22

Forest Change Assessment Viewer ......................................................................................................................... 24

Terra-i, A Near-Real Time Monitoring of Habitat Change ....................................................................................... 27

INDICAR ................................................................................................................................................................... 28

Early warning capacity by Synthetic Aperture Radar (SAR) .................................................................................... 29

Early Warning Systems for Fires .................................................................................................................................. 31

Firecast- Fire & Forest Monitoring & Forecasting System ...................................................................................... 31

The Global Early Warning System for Wildland Fire ............................................................................................... 32

Global Observation on Forest and Land Cover Dynamics GOFC-GOLD ................................................................... 32

Use of Active Fire Data Sets in Support of Fire Monitoring, Management and Planning ....................................... 33

Working Groups ........................................................................................................................................................... 34

Capacity Building Initiatives ......................................................................................................................................... 39

INPE Capacity Building ............................................................................................................................................ 39

Capacity building efforts FAO .................................................................................................................................. 40

ALOS PALSAR 25m Global Mosaic Data ....................................................................................................................... 42

Panel Discussion .......................................................................................................................................................... 42

Closing ......................................................................................................................................................................... 45

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Day 1: January 19, 2015 Welcome and Introductions

The 12th Regional Workshop began with a welcome and introduction from Dalton Valeriano, the Director

of the Amazonia Program in INPE and Thelma Krug from the Office of International Cooperation in INPE.

Dalton Valeriano and Thelma Krug expressed their thanks to all for attending. Thelma Krug described the

INPE headquarters and Brazil’s approach to monitoring deforestation. Brazil uses large amounts of

Landsat imagery as a tool for monitoring and producing annual wall-to-wall assessment of deforestation.

Capacity building is highly important to reduce deforestation within the country. Brazil has been

performing capacity building with other countries and through many partnerships such as with the U.S.

and FAO to make this a reality. Thelma Krug expressed how interested the Brazilians are in seeing what

other countries are doing through this workshop.

The Consul General Dennis Hankins from the U.S. then explained the importance of having good science

behind policy. Both policy and science are essential to working towards change. Diplomats of all

countries are aware of issues and threats such as climate change and they are looking ahead to see what

can be done. Science is important to formulating the appropriate policy, and each country will decide

what is important to them.

Dennis Hankins went on to discuss Brazil and the benefits of this workshop. Brazil is currently tackling

multiple issues, and the hope is for this workshop to bring light to these difficult situations and provides

the scientific foundation necessary for moving forward.

SilvaCarbon Program and Global Forest Observation Initiative

Presenter: Doug Muchoney - SilvaCarbon, U.S.

Doug Muchoney presented on the SilvaCarbon program and the Global Forest Observation Initiative

(GFOI). Doug explained how the U.S. and Brazil have a long history of working together on many issues

including deforestation and degradation. The GFOI is a part of the Group on Earth Observations (GEO),

which is a voluntary association of governments and international organizations to leverage remote

sensing and spatial analysis for societal benefit. The goal of GEO before 2015 is to enhance the

coordination of efforts to strengthen individual, institutional and infrastructure capacities, particularly in

developing countries, to produce and use Earth observations and derived information.

GFOI has five components:

1. The coordination of satellite data supply through CEOS

a. This is fundamental to all of the GFOI objectives and CEOS supports participation from

all countries in reporting.

2. Capacity Building (U.S.)

a. This is implemented through the SilvaCarbon program, which conducts regional GFOI

workshops with the aim to showcase operational methodologies for different aspects of

forest monitoring, discuss new cutting edge research methodologies, and provide a

space for testing and demonstrating field methods using the new technologies.

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3. Methods and Guidance Documentation (Australia)

a. The Spanish version of this document will be available in February 2015.

4. Research and Development Plan (Norway)

5. Admin and Coordination (Programme Office)

The U.S. contribution to GFOI, SilvaCarbon, works to partner with countries to improve monitoring of

forest and terrestrial carbon as well as to improve understanding of methodologies, collection, and

dissemination of data. SilvaCarbon develops regional GFOI workshops in Latin America, South East Asia,

and Central Africa. SilvaCarbon is a multi-agency program including USAID, State Department, USGS,

USFS, NASA, EPA, NOAA, Smithsonian Institute, and Universities.

The objectives of capacity building are to enhance the capacity of countries to initiate forest and

terrestrial carbon assessment and monitoring and to use management methodologies and technologies,

as well as to strengthen the community of forest and terrestrial carbon technical experts. In the future,

SilvaCarbon will work to coordinate among GEO and GFOI partners and others on the development of a

comprehensive, yet flexible capacity building strategy.

Questions/Comments:

Thelma Krug (INPE) commented that it is very nice to see a capacity building program from GFOI

that is working on each country on an individual level and then coordinating between other

countries to discuss tools and current developments. It is important to note that what may work

for one country may or may not work for another country based on the individual needs.

Ake Rosenqvist (soloEO) added that GFOI very early on decided not to focus solely on REDD+.

GFOI is focused on the data sources which are available free of charge to the countries such as

Sentinel and Landsat.

INPE’s Brazilian Amazon Forest Monitoring Program

Presenter: Dalton Valeriano - INPE, Brazil

Presently, 81% of the Amazon forest in Brazil is still intact. Deforestation has been an ongoing issue for a

very long time, and there is a need for integrative policies in order to control it. In 1965, a Brazilian

Forest Code was developed establishing a protection area of 30 meters of natural vegetation along

either side of rivers. This is a very strict code and there have been issues enforcing it. In 2012, this code

was revised, which relaxed some of the strict rules. The best thing to come from this revision was adding

a need for rural areas to register the location of the protected forest areas.

INPE’s role is to be the information provider. The organization has several programs including PRODES

for primary forest deforestation, DEGRAD for degraded forest area, and DETEX for selective logged

areas.

For PRODES, the aim of the project between 1988 and 2002 was to produce yearly gross primary forest

degradation statistics at the regional and state levels using visual image interpretation of Landsat

imagery. From 1997 to 2003, INPE developed a SPRING based system using Landsat imagery, which

involved processing steps, database preparation, image processing, editing, rate calculation, and data

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dissemination. However, it involved multiple databases of imagery for each location which became

unmanageable. Then from 2004 to present a TerraAmazon system has been used, which is a more

manageable system involving a single unified geo-database.

There were a multitude of issues which lead to the development of TerraAmazon including the

instability of rate estimation due to clouds and image acquisition date, the compromise between

restriction of image acquisition date and availability of cloud free imagery, and the need for multi-date

capability, smaller cells and an integrated database. For the issues with clouds, TerraAmazon masks out

clouds from the imagery and then replaces the missing data for the area with another cloud free image

of a similar date.

INPE Assessment of Forest Degradation – DEGRAD and DETEX Presenter: Dalton Valeriano - INPE, Brazil

Dalton Valeriano explained the process of forest degradation to deforestation from traditional selective

logging (such as for roads), uncontrolled logging and fire (such as deliberate slash and burn), further fires

and finally to deforestation, where only dead logs remain and secondary growth is coming in behind it.

This process is shown below.

All of the INPE systems complement each other. For example, PRODES can be used to map

deforestation, while DEGRAD can map area of degradation for areas affected by uncontrolled logging

and fire and further fires. Finally, DETEX can be used to detect areas with selective logging. Though a

combination of DETER and PRODES early warning maps can be developed by analyzing time series with

DETER detecting the process of degradation and PRODES measuring the final result.

The results of DEGRAD shows hot spots, calculates the degradation areas, and analyzes the trajectories,

while DETER has the same concept, only using different data with higher frequencies. DETER exploits the

temporal resolution of MODIS, and uses the best set of MODIS True-Color Rapid Response Products.

Visual interpretation is used and is supported by PRODES and a cumulative DETER mask. The results are

then delivered to IBAMA (Brazilian environmental law enforcement authority at the federal level) within

5 days of the acquisition period.

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Brazil has a huge effort for

controlling deforestation, with a

large number of people involved

on both federal and local levels.

DETER is accessible by the public

online, and anyone who registers

can receive an automatic email for

specific areas showing when

change was detected. This data

are meant to help the law

enforcement agency by indicating

where to go in the field. The areas

with high degradation are able to

be detected with MODIS, and with

this form of data the whole of

Brazil is covered within 2 days. To

the right is a picture of the DETER

system.

Operation Applications of Early Warning Systems- DETER-B Igor da Silva Narvaes - INPE, Brazil

The objective of DETER-B is to generate early degradation warnings to support deforestation control

activities. All the tasks for the system are performed on TerraAmazon, which is a unified, multi-user geo-

database. The framework for the system is shown below.

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There are different ways to detect certain types of degradation. For example, clear-cut deforestation is

easy to detect as it has well-defined boundaries between the cleared space (bare soil) and the

untouched forest. DETER can also detect regrowth of vegetation in a deforested area, degradation,

mining, selective logging, and burn scars. Below are examples of detection for mining and burn scars.

DETER-B Field Data

Marcos Adami - INPE, Brazil

Three field campaigns were developed in 2013 in partnership with IBAMA in the municipalities of

Itaituba, Uruará, and Nova Progresso. The routes for this field work are displayed below.

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The mapping period was from April to September with field work conducted in November and

December. Out of 250 points verified, 210 (or 85%) were confirmed deforestation while 40 were in

disagreement (15%). AWiFS was able to detect clear-cut deforestation, mining, and selective logging.

Questions/Comments:

Brian Zutta (MINAM) asked how many people are dedicated solely to early warning systems or

deforestation in each of the centers in Brazil. Marcos Adami responded that around 14 people

are needed. Right now, INPE only has 8 people because they only have the trial using 2013

imagery. However, INPE does not need more than 20 people to work on early warning for the

Amazon.

o Cesar Diniz (FAO International Consultant) commented that quantity is not the issue,

but rather training. If the country had a few properly trained people in interpretation

that is all that is needed.

o Brian Zutta added that there is a lack of understanding of how many people are actually

necessary. It is believed that only a few people are needed for both deforestation and

degradation. While Peru is not as big, there are some things that Brazil has already done

for deforestation and degradation that Peru is just starting to do now.

Country Presentations Each of Latin American SilvaCarbon countries (Peru, Colombia, Mexico, and Ecuador) presented on the

status and/or plans of the country for implementation of an early warning system for deforestation.

Status of the MRV System and the Integration of Early Warning Systems for Deforestation in Peru

Presenter: Brian Zutta – MINAM, Peru

Brian Zutta discussed the MRV system, its

progression, and how it is linked to the early

warning system which is currently being

developed through the collaboration of

various agencies. Peru shares the Amazon with

Brazil, with over 65 million ha of forest. The

map on the right displays areas of

deforestation in red from 2000 to 2011, with

approximately 1.2 ha of forest loss. This area

of deforestation will be even larger when the

data from 2012 to 2014 is added, with around

140,000 ha of additional loss per year in 2012

through 2014.

Landsat imagery is used to detect areas of

degradation and deforestation in various

locations throughout Peru. Currently, Peru

looks very similar to what Brazil was going

through in the 1980’s and 1990’s, except for a

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couple of instances of palm oil plantations and gold mining in Peru within the last few years.

Brian Zutta showed time series of imagery displaying increases in these mining activity and palm oil

plantations. These plantations are considered non-forest in definition and are easy to detect as they

appears brighter on the imagery due to spacing between the trees and also because the areas all have

the same shape and pattern, which is much different from forested areas.

The MRV team has received funding from the Ministry for geo-processing and GIS and has moved into a

new office. This new office can hold up to 16 people and there will be a new server to disseminate the

data. REDD+ has caused MRV to grow, and it has been divided into four major areas:

# Major Topic Areas # of Hired Full-Time Technicians

1 Deforestation 1 (Will have a couple more in future)

2 Land use/Land use change 7

3 Carbon (Carbon maps/calculations) 2

4 Degradation, Reference emission levels, ect. 1 (Will have a couple more in future)

These individuals have helped develop a roadmap (below) for how long each initiative will take for the

Ministry and the NGOs to look at deforestation in the Amazon and other areas in Peru.

All of this has helped to build up the capacity and interest for early warning systems. The system, Terra-i

Peru, is a portal to see what is occurring in different areas. Right now the system is still in its early

stages. The central government has heavily utilized this system, but there has still not been a link to the

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local and regional governments/players. This system uses MODIS data, which is very coarse, but still

provides useful information.

MINAM is using early warning systems for detection in community areas. For example, the data have

been used to detect a road in the image below which was built through a community area.

While the data are very coarse and delayed, it is the hope that in the future Peru will be able to detect

this and alert the locals of this change. There are future concerns that this road will continue more into

the local area and disrupt indigenous populations, so this must be monitored.

For the future, Peru wants to continue to advance these systems and integrate them for land use

change. Peru is very interested in Brazil’s approach of having both a coarse and a fine system with

DETER-A and DETER-B, and would like to investigate this in the future as well as integrating multiple

systems. Peru would also like to integrate fire warning and improve the flow of communication to

regions and communities.

Questions/Comments:

Ake Rosenqvist (soloEO) asked if Peru has plans on using SAR in the future, to which Brian Zutta

responded that while there has been training on radar, there has yet to be any discussion on

how to use it. There is a desire for using radar in the future, but there are not any current efforts

to incorporate this data.

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Status and Plans for Implementation of an Early Warning System – Colombia

Presenter: Gustavo Galindo – IDEAM, Colombia

Colombia’s forest monitoring system has three important parts.

1. Deforestation quantification for gathering forest area data as well as the changes in these areas.

2. Carbon monitoring for gathering information about carbon stocks for greenhouse gas emissions.

3. Deforestation Early Warning System, which has become an annual process since last year.

Colombia has an approach for MRV that has to be coherent on the subnational level. They started

constructing different protocols for each of the regions to apply at the local, regional, and national

levels. Due to all of this, Colombia has worked with a lot of different imagery. Colombia was working

with around 100 images for the whole country through 2010. Since 2010, this number has increased to

700-800 images for an annual report. Colombia had to change the way it was processing the imagery

and working with the database as well as change how the datacenter was doing processing.

For the annual deforestation rates, Colombia had 162,000 acres of deforestation in 2013. Most of the

deforestation is in the Amazon region (57%), while 3% are areas without data. These areas with missing

data are complete the next year.

For early warning systems, Colombia started working with ALOS for ScanSAR images to try to detect

changes in forests automatically. Around 50 images were processed between 2008 and 2010 with

gamma software. There has been mixed results, but there is a lot of possibilities for it in the future and

Colombia is looking forward to working with it more. Early warning has become very important, in some

cases more important than deforestation results. People of local communities are very worried about it.

Colombia is doing this in a different way than Brazil. The country is making mosaics and then taking out

the problematic pixels, which is a mostly automated system. Colombia is working with software called

TISEG for pre-processing the data. All the processes for deforestation and early warning system always

end with manual interpretation and Colombia is not relying solely on automated results. This can be

done in two weeks, but in Colombia there are some months that are fully cloud covered, so the

minimum period for having results is 6 months.

Colombia is seeing that in less populated areas with more forest there is less capacity and therefore less

of a reaction from the results, while there is more deforestation present. So Colombia repeats early

warning every 6 months in these areas where there is more change happening. There are also a lot of

illegal activities in these areas, so it is very complicated.

Colombia is looking at ways that they can communicate directly with the government and the local

communities. Local areas are being taught how to download the images, do basic processing with the

software, and also how to do a visual interpretation every 15 days. Communities have not received any

support for the forests. They need to monitor it to receive funds.

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Questions/Comments:

Inge Jonckheere (FAO) asked about sustainability of the imagery they are using and what the

option is if they do not get a good price. Gustavo Galindo responded that Colombia is currently

working only with MODIS and they have to work with other options like Sentinel. Colombia has

to consider how often they need to do this, as every six months may not be feasible. It is very

important that there is currently a lot of support, but they know that it will eventually end.

However, the forest monitoring system has to be sustainable, and they are working to find the

best way for this.

Ake Rosenqvist (soloEO) commented that in the near future Sentinel-2 will be available. This will

be a game changer because it will have very high resolution data with high temporal resolution.

The challenge is how to handle this vast amount of data. Sentinel-2 has some of the greatest

promise for forest monitoring, and Ake believes there is a place for radar in this. Any detection

system should not be built on one or the other system, but rather both. Radar can be used to fill

in the gaps. If a country is at the beginning of developing their system, they should include

radar. Radar is very different, but not necessarily difficult.

Early Warning Systems – Mexico

Presenter: Sergio Villela – CONAFOR, Mexico

In Mexico, the national implementation of REDD+ MRV system requires a strong and solid inter-

institutional cooperation at the federal level, including actors from academia and stakeholders at the

state level. The working group has nearly 40 people comprised of the following organizations:

Organization Responsibility

CONAFOR (National Forestry Commission) Generates land cover change reports

INEGI (National Institute of Geography and Statistics)

Provides national regulations for cartographic products and geospatial data

CONABIO (National Commission for the Use and Knowledge of Biodiversity)

Specializes in remote sensing processes in MRV activity data

The new automatic system, MAD-MEX (MRV activity data in Mexico) provides timely delivery of accurate

maps within a few days. It produces domestic land cover change maps at different scales (1:100,000 and

1:20,000). The products which are generated automatically from MAD-MEX are then edited, revised,

and validated to complete annual reporting for forest emissions of greenhouse gases. An issue from the

beginning has been the size of Mexico and the variety of different species which has created multiple

problems for developing an automatic classification system.

The objectives for Mexico are first to generate automatic land cover maps with classifications of Landsat

imagery from 1993, 1995, 2000, 2005, and 2010, and then with RapidEye imagery from 2011 onward.

Mexico also wishes to obtain land cover change activity data to overlap the Landsat classifications for

the years 1993-2010. The other objectives are to generate the historical deforestation rate for 1993-

2010, to obtain annual land cover change activity data with RapidEye imagery, and finally to obtain a

high accuracy classification for generating a finer classification scheme through post-processing.

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RapidEye imagery has been used because of its high spatial resolution, the ability to pre-process data

with high geo-location accuracy, and its constellation of satellites with the likelihood of multi-temporal

coverage.

The classification process for MAD-MEX has been performed at different scales to determine land cover,

land cover change, forest/non-forest, forest change, and cover density through automated wall-to-wall

mapping and “map-to-image” with the use of historical land use cartography. The challenge with MAD-

MEX is being able to generate domestic products within 1 year due to the number of available staff from

the three organizations. Since 1990, when Mexico first started its national inventory, the country has

tried to consistently report on a five year cycle.

The map below shows the results of the MAD-MEX system with the use of Landsat data for the year

2000. The different colors represent the different land covers throughout Mexico.

Questions/Comments:

Jennifer Hewson (Conservation International) asked why RapidEye imagery was used and if

Mexico is buying the RapidEye data directly. She also asked if there is any discussion going on

about the sustainability of the system with RapidEye as the long term commitment of the

satellite may not be as good as systems like Landsat or Sentinel. Sergio Villela responded that

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Landsat was used in the beginning but then an agreement was signed with RapidEye because it

can be used in both the dry season and the wet season, which is very good for areas such as the

Yucatan. Conabio bought the RapidEye imagery for all of the agencies in Mexico. He also stated

that the people in Mexico using the imagery have chosen RapidEye because of various issues

they have discovered while classifying with Landsat imagery.

Guillermo Sanchez (USFS) asked if RapidEye is being used for the whole country. Sergio Villela

responded that it is for the entire country. He is not sure of the price, but the imagery is still

better for Mexico, because they have to use a different scale.

Cesar Diniz asked if Mexico has tested using the panchromatic Landsat 8 band. Sergio Villela

responded that while he believes the group did, the issue is that higher resolution is needed for

the variety of projects and classes (115) that they have.

Ake Rosenqvist (soloEO) commented that the higher resolution of RapidEye can provide more

information for areas with increased woodland areas instead of tropical forests.

Jennifer Hewson added that while dry forests can be a big issue, Mexico should consider using

Landsat for the general information and RapidEye for the dry forests.

Status and Plans for Implementation of an Early Warning System – Ecuador

Presenter: Andrea Bustos and Nestor Acosta – MAE, Ecuador

Ecuador discussed its developed MRV system in terms of REDD+.

Measurements:

For the baseline variable, wall-to-wall maps of the country were developed for 1990-2000-2008-2014.

The deforestation rate was also calculated between two periods: 1990-2000 and 2000-2008, which

showed a reduction in deforestation. The country has also created a huge project for ecosystems, which

started in 2010 to measure fragility and fragmentation.

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For the forest national inventory, both carbon and base area maps were developed from 2012-2014.

The ecosystem maps were also used to make better estimations of the nine carbon strata. From these

nine strata there were 900 plots. From the national forest inventory, Ecuador has learned that they want

to make better decisions for placing plots so they are trying to redesign the permanent plot system. It

was discovered that some of the plots were not in the conservation area, resulting in the loss of some

data. So the plots will be redesigned and the grid will be changed from 100km to 100m.

Reports:

In the reports safeguards have been implemented with mainly

Socio Bosque. This project began in 2008 and is still ongoing.

Since 2008, over $24 million USD has been invested. There are

efforts in reforestation, and by 2017 Ecuador wants to

implement a reforestation area of 1,500,000 ha. Methodologies

are still being developed for how and where the reforestation

will be measured.

Also in the reports, there will be a possible addition of

sustainable forest management. For example, Ministry of

Agriculture is working on zoning and local planning issues as

shown in the map to the right in the hope of reducing

deforestation from the exportation of palm oil.

Verification:

Each of the processes has their own method of verification.

Early Warning System:

A conceptual model has been developed but not yet implemented. The image below demonstrates this

model.

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In the first portion, there is an automatic remote sensing process to establish some agreement to

develop the algorithm for the model. The next part shows the action of the community (i.e. park

rangers) with the use of smart phones and the internet to introduce local information, as well as a call

center where workers from the Ministry can call and give the information in a web portal. The idea is

that when a fire, illegal logging, etc. occurs people can call and enter the information in the applications

or in the webpage to add the data into the system. This system should alert the authorities of those

specific problems. This information can be used to validate info generated in the automatic process.

The screen capture below shows what the system will look like for adding information.

Questions/Comments:

Pontus Olofsson (BU) asked how Ecuador intends to use the MODIS/Landsat data to monitor

change in near real-time. Nestor replied that this is one of the questions that they have, and

they are trying to work through it. Ecuador is studying using all the data and then developing

step-by-step procedures.

Pontus Olofsson added that Boston University has funding from NASA to try out their data, and

they would be interesting to testing it in Ecuador.

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Day 2: January 22, 2015

EWS Methodologies Near Real-Time Mapping of Forest Disturbances

Presenter: Eliakim Hamunyela, Wageningen University

Eliakim Hamunyela explained that medium resolution data should be used for early warning systems

because small scale forest clearings will be the main challenge in the future, and this level of resolution

is necessary to see these changes.

BFAST-Monitor has been used in the past for near real-time change detection by taking all available data

and modeling the behavior for the location. Then when a new image becomes available, it adds the new

information. This approach takes data from the forest and makes out vegetation activity over time.

However, there have been problems involving monitoring dry forests and most of the time there is not

enough imagery to model the behavior. With this issue, the user is unaware if the forest is being

modeled or if it is noise. It is very difficult to point out abnormalities, so Wageningen University has

been working on a new approach.

This new method is called the spatial context approach, which makes it easier to see disturbances in

time series and therefore change can easily be pointed out. This approach has been tested at sites in

South America (shown below) using Landsat 5 and 7 NDVI time series from 1984 to 2014 in both humid

and dry forest areas. The test was to determine if change can be seen easily. The results found that the

approach works very well in dry forests and change is detected earlier. The results had fewer

fluctuations as compared to BFAST.

Humid forest

(evergreen)

Dry forest

(deciduous)

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The future is about computational power and how to deal with large amounts of data. Wageningen is

working to put these approaches into platforms like Google Earth Engine for better optimization. The

image below shows BFAST Monitoring in the Google Earth Engine.

The involvement of local communities is also crucial. There is enough technology now to involve local

people in community monitoring. Wageningen is working on projects where data are sent from local

areas to action centers. In the future, it is believed there will be a fusion of Landsat and Sentinel-2 data.

The differences in the sensors will not have a large impact and the data will be fused with minimal

issues.

The overall conclusion from this presentation was that for dry forests and developing early warning

system it is best to use the spatial context approach. It is also important to try to involve local

communities, as this is a more sustainable approach. Fusing data will play a key role in the future of

early warning systems, but access to computational powerful platforms will be crucial to making this

work.

Questions/Comments:

Jennifer Hewson (Conservation International) asked that given the computational requirements

of the spatial context approach, would it only be suggested to use for areas with dry forests.

Eliakim responded that it is difficult to detect change in time series. It is a tradeoff, if the

individual wants to immediately check if something is different in the forest the spatial context

approach should be used. If the individual can wait a few months for the change data, then it

should not be used.

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Pontus Olofsson (BU) asked why NDVI was used for their test sites. Eliakim responded that it

was not used because it was the best data but because the main focus was to determine how

the approach dealt with the fluctuations.

Pontus Olofsson added that Sentinel-2 is a very promising mission but it will take a long time for

the data to be useable for South America. The plan is to start with Europe then Africa and then

South America for the collection. NASA and USGS will work to integrate the data into their

systems to be compatible with Landsat.

Two MODIS-Based Approaches for Monitoring Forest Change in Near Real-Time

Presenter: Pontus Olofsson, Boston University

Boston University has received research funding from NASA (The Science of Terra and Aqua) and USGS

for two MODIS-based methodologies and is looking to partner with tropical countries. Pontus Olofsson

discussed the problems with using MODIS. While it is an impressive dataset, it lacks a change detection

product. NASA funded researchers to develop change detection products, but it failed because of the

observational scenario from MODIS. The observations do not always match the locations of the grid cells

in MODIS. This image shows a 500m MODIS pixel, where the ellipsoids are the observations for the same

cell in a single week. These ellipsoids do not align until after 16 days, when MODIS repeats the same

flight path.

It was also found that time is less important than view angle. For example, there can be an image from

one day and the image from the next day is not as correlated as one that is collected later because of

the difference in view angle. The greatest distance occurs at 8 days, and then at 16 days it is back in the

same flight track.

A potential solution is to use the time series to predict what the next observation would look like. If the

predicted observations are compared to the actual observations and there is a difference which is

greater than the threshold for consecutive time steps it would infer that change has occurred.

Image from Xin et al. (2013).

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Two Approaches:

1. Direct Approach- this uses the dense time series of MODIS filtered by zenith angle to predict the next

MODIS observation.

2. Fusion Approach- this approach would use Landsat time series data to predict daily Landsat images to

recreate the MODIS acquisition process.

The change for both of these approaches would be inferred by comparing the predicted to the actual

observations.

Continuous Monitoring of Forest Change in Near Real-Time with Data from the MODIS Sensors

Presenter: Pontus Olofsson, Boston University

For the first approach, the basic idea is to take Landsat-based methods and run them with MODIS data.

For these Landsat methods Boston University teamed up with USGS to determine how to use the

Landsat data. USGS developing Land Change Monitoring, Assessment, and Projection (LCMAP), which

looks at the reasons for land cover and land use changing, how the change has varied over time, the

drivers for the change, and whether or not it is possible to detect both historical change and current

change consistency and with similar accuracy.

The essence of the algorithm is not that different from BFAST, as it monitors patterns in the

observations to predict change. If there are enough observations throughout the seasons a prediction

model can be developed and new observations can be added to the model as they become available. As

shown in the image below, a change is occurring between 2000 and 2002 as there is a change in the

pattern of the data.

The pattern has changed, so it should be assumed that the next observations (red) will follow a different

trajectory than previously and will behave differently than before. This change can be detected with a

built-in mathematical expression and then the time series can be broken at that point. A new time series

can then be fit to the new (red) observations once there are enough observations. The coefficients from

this model can then be classified (green as mixed forest and red as residential). This algorithm will ignore

individual outliers. The idea is to start this in the U.S. and then eventually expand to other countries.

It has been proposed to NASA that the algorithm would be applied to MODIS data rather than Landsat.

There would be near real-time benefits to it, but the issue is that the prediction is done in multiple

bands of Landsat, while in MODIS there are only two bands at 250m resolution with the other bands at

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500m. It was tested and found that the 500m bands were not useful for detecting change. The two

250m bands are red and near-infrared. While there is obvious forest change with the near-infrared

band, it is still not helpful on its own. The best option is to use two bands: the red and also the red/near-

infrared. There is also the issue of the large zenith angle, so it was decided to eliminate all observations

over 25 degrees. In the upcoming semester, the plan is to weigh the observations, where the big blurry

pixels will have the lowest weights.

Future improvements:

In the future, BU will need to pay attention to the view zenith angle, and there is a desire to investigate

the use of the MAIAC data for both modeling and cloud screening. BU will look into the possibility of

adding VIIRS data into the time series and investigate how well it fits the MODIS time series. Also, BU

will need to figure out how to classify and screen the areas of non-forest and the best plan for

implementation in the tropics and other areas. Pontus added that if any country would like to work with

Boston University, they would be happy to work together, such as in Ecuador.

Questions/Comments:

Marcos Adami (INPE) asked how something like this could be done since there is a lot of Landsat

imagery for Brazil. Pontus replied that it would take a lot of processing power. Currently, USGS

has the capacity to do this, but it would be a lot for the Amazon.

Gustavo Galindo (IDEAM) asked if there is a difference between the Aqua and the Terra data.

Pontus answered that he had not seen any difference in the data used in the U.S. However, this

is not the case for the tropics. There is a difference, and there are fewer observations for Aqua

in the tropics due to fewer overpasses.

Near Real-Time Monitoring of Land Cover Disturbance by Fusion of MODIS and Landsat Data

Presenter: Pontus Olofsson, Boston University

This methodology assumes that the Landsat processing has already been performed, and then the

MODIS data can be added on top to detect disturbances. It creates synthetic images using the prediction

model and then uses the synthetic imagery as the ground surface so it can recreate the MODIS swath

observations based on the Landsat observations to predict future observations. The actual MODIS swath

observations are then compared with the synthetic swath to detect land cover change in near real-time.

Currently, BU is assessing the performance of the MODIS cloud mask, while looking into alternatives and

improvements. They are also continuing to develop and improve the original fusion prototype for

change detection and are using a study site in Canada to test the model on forest degradation and

beetle infestation. The fusion model is currently being tested for performance in a study site in Acre,

Brazil.

For the cloud issue, MODIS has an internal cloud mask which can eliminate regular clouds. However,

small and thin clouds are often missed, which causes the model to flag false changes. There are also a

limited number of clear observations in the Amazon, which will affect the quality of the synthetic

Landsat image.

For the test site in Acre, there is not a lot of clear imagery available for this area even with a

combination of Terra and Aqua. These clouds are causing issues with the analysis. There is a big issue of

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residual clouds which are interfering with the ability to detect changes. In the test site, BU used all

available Landsat data to do analyses and then created synthetic Landsat data. A shift was found in

1988, showing a possible flooding area in the north of the site.

In conclusion, change can be detected with the fusion of the MODIS and Landsat. The internal cloud

mask for MODIS does a decent job, but small clouds can be missed. The clouds can affect the ability of

the approach to detect change in near real-time.

Questions/Comments:

Gustavo Galindo (IDEAM) asked how long the approach can predict and still be accurate as it can

be months before clear imagery is available for some South American countries. Pontus

answered that there is a threshold, and Colombia is reaching that threshold. There is a

combination of a lack of available Landsat data and the available imagery being cloudy. There is

a need to implement a way of weighing of the observations.

ForWARN: A Cross-Cutting Forest Resource Management and Decision-Support System

Presenter: Bill Hargrove, U.S. Forest Service

The ForWarn system was a joint effort by USDA, NASA, and Oak Ridge National Laboratory to help

monitor threats to forests. ForWARN is MODIS-based and covers the contiguous United States to

generate new potential disturbance maps every 8 days. The system detects all types of forest

disturbances, including insects, diseases, wildfires, ice and frost damage, tornadoes, hurricanes,

blowdowns, harvest, urbanization, and landslides.

ForWarn has been in operation since January 2010, essentially covering 100% of the forest every 8 days.

The system has an online Forest Change Assessment Viewer, which is the main form of distribution for

the ForWarn system.

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This system was developed for free by the University of North Carolina Asheville and is very similar to

Google Maps. There are many other ancillary maps in the same spatial context which can be used.

ForWarn works by comparing the current satellite greenness with a historically “normal” observed

greenness to find potential disturbances. The areas will less greenness than expected are determined to

be disturbed, while areas with more actual greenness than expected may show vigorous or recovering

vegetation. While the system only shows forested areas, ForWarn can detect all vegetation.

The normal or expected greenness value is both spatially customized for each map cell and also

temporally customized for each 8-day period. Every map is a percentage of expected greenness, with

less than 100% of expected greenness showing potential disturbances as green, yellow, or red. Greater

than 100% expected greenness shows vegetation recovered, which is shown as blue. The image below

shows a ForWarn image from June 1, 2011 displaying tornado damage in the U.S. The damage is easily

detected with a red core and yellow boundary.

Three slightly different national disturbance maps are created every 8 days. The differences are related

to the age of the disturbances mapped. A short-term history of the prior year depicts recent

disturbances. A mid-term history of the last three years shows intermediate-age disturbances. Finally, a

long-term history for the entire baseline period shows all disturbances since MODIS began. ForWarn can

see the extent of a disturbance, but also detect the recovery from it. It does not simply pick one

definition of normal, but three and allows the user to pick the definition that is most relevant to them.

Two great features are the Share-This-Map feature and the Pest Proximity Feature. Share-This-Map

creates a URL that the user can send to another person, who can click on it to see the same extent and

layer that the person was previously viewing. This facilitates communication and consultation with the

ForWarn team. The Pest Proximity Feature combines all pests/insects/diseases in an area so that when

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the user clicks a certain point it shows all of the usual suspects (all insects/diseases that have been seen

near the point) to show all the likely disturbances. The user can also click on a location to get a graph

which shows the annual phenological track/profile. ForWarn is not measuring disturbances but rather

departures from normal phonological timing. It can detect weather departures from deviations of

precipitation and temperature on top of wildfires, pests, etc.

In 2014, three new ForWarn products were added. Two of the map products are ways to compare

current greenness with a seasonally normalized baseline, while the third is a new early detection map

which helps detect even more quickly. This uses adaptive length compositing to shorten the detection

times. Detection delays are caused by issues with clouds, which can cause false positives. There is a new

adaptive length compositing algorithm for current eMODIS image, where the blue band is used as a new

cloud-detection algorithm to objectively define a good look. If the blue value is too high, the value is not

used.

In the future, ForWarn will have a new cluster-derived baseline product, which will be the first non-

historically derived baseline for ForWarn. Another thing USFS is working on is backwards in time

processing to produce ForWarn products for the past, working back to 2000 imagery to create an instant

history which will provide more experience detecting additional types of disturbances to better

understand current disturbances. USFS is also building towards a set of subscription services, where

users will get alerts for nearby disturbances through social media. This is in high demand as users will

not have to keep checking the ForWarn system for change.

Overall, the goal for ForWarn is to act as an alarm for forest disturbance. Ultimately, USFS wants it to

convince forest managers to use ForWarn themselves to monitor their own forests. This is a great way

to establish a two way communication and a working partnership with forest managers.

The slides for this presentation are available at: http://www.geobabble.org/~hnw/first/brasil1

Questions/Comments:

Jennifer Hewson (Conservation International) asked how USFS is actually attributing the causes

of forest disturbances. Bill Hargrove responded that it is a protocol the USFS use that relies on

first answering the question of whether or not there is a real disturbance, but then compares

with many ancillary maps in the viewer. The comparison can usually get a good first order guess

on the cause of the disturbance. This is beyond the intended function of the system as it was

developed to act as a smoke alarm, but it does provide a cause and is almost always right.

Forest Change Assessment Viewer

Presenter: Bill Hargrove, U.S. Forest Service

The Forest Change Assessment Viewer is the main delivery vehicle for output from ForWarn. It is a free,

open, browser-based system which can see natural and human disturbances as well as recovery. It uses

moderate resolution satellite data to provide forest change recognition and a tracking system. USFS

tried to make the viewer as self-explanatory and user-friendly as possible. Whole groups of ancillary

information can be added, which allows users to have access to a multitude of information without

cluttering the interface.

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In addition to having the viewer as a distribution device for ForWarn, USFS also have WMS, Web Map

Service and WCS, Web Coverage Service, which is aimed at higher GIS users. Smart phones can be used

to subscribe to all of these services. There are also trainings available on the capabilities as well as more

detailed webinars on the viewer. Shorter videos are also available on how to use particular features.

The viewer has a graph NDVI tool, which shows the NDVI change of a location through time. The viewer

also has multivariate geographic clustering to statistically create and draw homogenous regions with

respect to the phenology. USFS has done this across all MODIS data years for the 48 U.S. states. The

viewer can show maps of phenological ecoregions (phenoregions) which represents the best vegetation

type maps every 8 days over a 13 year period. If the phenology has ever acted differently, this can be

seen on the map. The map below shows the 50 most different national phenoregions for 2012.

USFS used the same multivariate approach globally with clustering of MODIS fire hotspots to examine all

the hotspots ever collected and aggregated them into 10km2 cells for all fires (human-cause and

wildfires). Clusters were developed for the 1000 most different fire types globally. This provides a

synoptic global perspective to see the coarse dynamic of fires.

USFS is also doing temporal unmixing of the phenological signal to separate evergreen and deciduous

forests. In the winter time, the only contributor to forest greenness is evergreen vegetation, this

constant seasonal evergreen contribution is quantify and subtracted from the total NDVI to separate

evergreen from deciduous. Then evergreen and deciduous vegetation can be mapped separately.

Declining and thriving forests can be located by performing a 150 million per-cell temporal linear

regression through the 11 yearly minimum and maximum NDVI surrogates. From this, a slope is

produced which shows the long-term trend in forest health. The image below shows the evergreen

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thrive and decline of the forests in Western North Caroline between 2000 and 2010. There is nearly no

evergreen thrive present.

Slides available at: http://www.geobabble.org/~hnw/first/brasil2/.

Questions/Comments:

At the end of his presentation, Bill Hargrove commented that as countries develop their early

warning systems they should feel free to ask questions as the USFS has made a lot of mistakes

during the process. There will be a lot of challenges with the clouds, so it will be an interesting

topic.

Day 3: January 21, 2015

Field day organized by INPE to Parque Estadual Serra do Mar.

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Day 4: January 22, 2015

Terra-i, A Near-Real Time Monitoring of Habitat Change

Presenter: Oscar Bautista, Terra-i

Terra-i is a MODIS-based mapping tool to detect areas of rapid habitat change using an NDVI prediction

methodology. Terra-i has data from 2004 to now and is currently covering Latin America and the

Caribbean. It has web tools that allow the user to visualize and download data for habitat loss. The goals

of terra-i are to monitor the conversion of natural habitats in near real-time, have a continental

coverage of all types of habitat, be a support for government agencies in making decisions, quantify

habitat conversion rates and make analysis of trends from 2004 to date, and monitor the impact on

protected areas in Latin America.

The below image demonstrates the workflow of the terra-i system.

Calibration of the data is performed using Landsat imagery due to a limitation in spatial accuracy with

the MODIS data. Terra-i produces vegetation change maps every 16 days. The results were compared

with deforestation data produced by INPE from 2004 to 2009 through PRODES. There is a high

correlation between PRODES and terra-i. Terra-i has free for downloading data and the tool was

developed for non-GIS experts. However, expert users can download the data in a raster format.

The application can be used to monitor the expansion of large areas crops, to understand changes on

the field for validation, to detect changes in other ecosystems different than tropical forests, for

increase products (vegetation increase detection), and for integration to other policy support systems

(i.e. terra-i can be used for land cover change in hydrology).

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This system has had a lot of impact. For example, terra-i is now working with the government in Peru,

producing monthly data on a regular basis to evaluate their policies and the effectiveness of their

actions in the protected areas. Terra-i also cooperates with independent media, such as InfoAmazonia,

in exchanging data to produce monthly reports. Terra-i’s website currently has 1,500 users from 250

institutions. The users are mainly located in the U.S. and Colombia.

In the future, the terra-i data will be integrated into the Global Forest Watch platform. Terra-i also plans

to expand pan tropically. The image below demonstrates these plans for expansion.

In conclusion, terra-i is a mapping and monitoring system for near real-time assessment of land cover

conversion at medium scale. It is a tool that can be used at the national, regional or continental level. It

is useful for understand the effectiveness of the conservation policies, and it provides a spatial support

system for decision makers. However, terra-i is not a detailed monitoring tool for the local level. For this,

it requires high resolution imagery and field data. It also cannot monitor degradation.

INDICAR

Presenter: Edson Sano, IBAMA

INDICAR is a radar-based system for indicating new deforested areas in the Brazilian Amazon for law

enforcement activities. By using PALSAR data new deforestation can be detected around 61 days before

it is detected with MODIS. As shown by the image below, the raw PALSAR data are collected and sent to

the Earth Observation Center in JAXA within 24 hours. Within the next 5-7days JAXA sends the data to

IBAMA, the Brazilian Institute of Environment and Renewable Natural Resources.

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For the Brazilian Amazon, there is full ALOS ScanSAR coverage every 46 days. The PALSAR HH signal can

locate areas of potential deforestation. Between September 2009 and March 2011, 1,382 polygons were

flagged as potential deforestation. Of these, 120 polygons were verified as illegal deforestation.

Currently, there is no information on the rate of false alarms due to a lack of knowledge of how many of

the flagged polygons were validated in the field by the IBAMA law enforcement team.

In conclusion, due to cloud cover, the ALOS-1 ScanSAR system allows for the detection of new

deforestation two months earlier than by MODIS.New deforestation is not unequivocally detected in

ALOS-1 ScanSAR HH-pol images. Two opposite brightness patterns demand more attention from the

interpreters.

Early Warning Capacity by Synthetic Aperture Radar (SAR)

Presenter: Ake Rosenqvist, soloEO

SAR is an active microwave sensor, which can be used day or night and can penetrate through clouds

and smoke. However, SAR is not entirely weather independent as environmental conditions can affect

the backscatter. The important ground parameters are di-electric properties (water content) and target

structure. A general rule for the di-electric properties is that with increased water content there is

increased backscatter. For the different target structures, they can result in different backscatter

mechanisms.

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The straight-forward approach for forest mapping capacity of SAR is that maps change in reflectance

(backscatter) between subsequent image pairs in a time series. Ake Rosenqvist demonstrated this with a

SAR time-series, showing forest loss in red and forest gain in cyan.

Ake also discussed the potential of the ALOS-2 ScanSAR for early warning, which has been in operation

since November 2014. The system covers South and Central America every 42 days and has HH+HV dual

polarization, allowing for better discrimination of deforestation over HH only. The system also has 50m

GSD, which allows for detection at the 1 ha scale. Overall, SAR is a complement to optical early warning

systems. It has a lower temporal resolution than other systems like MODIS, but it is important in cloud-

prone areas.

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Early Warning Systems for Fires

Firecast- Fire & Forest Monitoring & Forecasting System

Presenter: Jennifer Hewson, Conservation International

Conservation International, an organization founded in 1987 with 869 current employees, built the

Firecast system which is an integrated web-based tool to facilitate decisions. This system uses near real-

time Earth Observations to send alert information on fires, risk of fires, and disturbances to decision

makers. This system can also generate information in multiple languages and is aimed at responding to

user’s needs.

The end user products include the custom active fire

alerts, the drought/forest flammability index, and the fire

season severity forecasts. All of these can be found at

http://firecast.conservation.org. Users can subscribe to

this free service to receive email alerts, text files, KMLs,

and JPGs of the fire location. The GIS data used in the

system reflect the needs of the end users.

In Madagascar, fire is a serious threat to the habitat of

endangered tortoise species. A pilot was done to see

how quickly and effectively villages could respond to and

control active fires. Cash prizes were awarded for

development projects to improve schools, build wells,

and purchase solar panels.

Firecast includes a forest flammability risk model which is derived from satellite observations of rainfall,

temperature, and relative humidity. The products available include the actual risk index and daily rainfall

information. An example of the use of this model was by FAN, a conservation organization in Bolivia,

who uses the data to put into their own alert system (SATRIF), where the alerts were then disseminated

to local farming communities to inform on timing of burning.

The system also has a fire season severity component, which provides expected intensity of fires based

on weather conditions during the upcoming dry season. There is also an outreach and engagement

component, which focuses on understanding the needs of decision makers, engaging with government

institutions, providing capacity building, and soliciting feedback to improve the system.

Firecast Phase II is a three year NASA-funded project which will focus on system enhancements, new

products, exchanging mobile data, and further outreach. The intention is not to be a global system, but

to work in areas where CI has a strong field presence. Phase II will include Bolivia, Peru, Colombia,

Madagascar, and Indonesia. The system enhancements add near real-time fire products and burned

area products, provide alerts for deforestation and illegal logging, enable mobile data for validation

purposes, and expand fire risk and fire season severity forecasting. There are outreach activities in

Madagascar to integrate with the New Environmental Plan and in Indonesia to support projects of

peatlands management and to partner with Global Forest Watch in their fires platform. There is also

activity in Bolivia with FAN to facilitate the adoption of the system and to improve forest flammability

alerts.

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For the future, there are proposed activities in Peru for case studies to demonstrate the impact of fires

and raise awareness, as well as to link with the REDD+ monitoring in Alto Mayo. There are also plans to

expand the alerts to Colombia. IDEAM is interested in integrating monitoring by local governments and

communities with alert systems such as Firecast.

The Global Early Warning System for Wildland Fire

Presenter: Bill de Groot, Natural Resources Canada

The Canadian Forest Fire Weather Index (FWI) system has been used in fire management since the

1970s. It is a global system with national applications in Europe, Asia, Southern Africa, North, Central

and South America, and the south Pacific region. The FWI system has 6 components including 3 fuel

moisture codes and 3 fire behavior indices. The fuel moisture codes are a simple accounting method to

keep track of fuels and the three codes are fine fuel moisture, duff moisture, and drought.

This whole system is entirely weather base.

Most of the fire systems are weather systems.

There are also fire behavior indices, which are

indicators of fire spreads, amount of fuel, and

fire intensity. Fire behavior integrates fire

weather, fuels, and topography.

The regional EWS prototype for Central and

South America was shown (right) which is based

in MODIS hotspots. For every hotspot there is a

pattern and from that pattern a prediction can

be created.

Often this system includes other remotely

sensed data, land cover, fuel information, and

information on the type of forest. To model fire

behavior emissions, the emissions are calculated

nationwide every year in Canada. Three fuel

consumption components are used:

aboveground, dead woody debris, and forest

floor. MODIS hotspots are used to determine

the daily fire activity. Then the group

interpolates the fire weather through the FWI

System to each pixel, and burns each cell using a

FWI-based fuel consumption algorithm.

Global Observation on Forest and Land Cover Dynamics GOFC-GOLD

Presenter: Wilfrid Schroeder, University of Maryland

GOFC-GOLD is a coordinated international effort consisting of a network of partners to improve the

quality and availability of observations of forests and land cover at regional and global scales by

producing and sharing useful, timely, and validated information products. GOFC-GOLD Land Cover & Fire

is a Panel of the Global Terrestrial Observing System GTOS (FAO GTOS Secretariat) and part of the Group

33

on Earth Observation (GEO). The group produces fire and fire related products for active fires, burned

areas, fire radiative power, global fire emissions, etc.

The GOFC-GOLD Fire Implementation team works with GOFC-GOLD Regional Networks to execute and

design projects and to develop consensus algorithms and methodologies for product generation and

validation. The Regional Networks connect researchers and data users, cater to user needs and foster

the transfer of technology, strengthen the involvement of local scientists, share regional data, secure

funding for sustained continuity, and improve and extend outreach activities.

Use of Spatially Refined Remote Sensing Active Fire Data Sets in Support of Fire Monitoring,

Management and Planning

Presenter: Wilfrid Schroeder, University of Maryland

At the University of Maryland, mid-infrared (SWIR) data are used to detect active fires. Wilfrid discussed

the evolution of fire mapping in near real-time. In 1980, fire mapping started with AVHRR 1km data for

12 hour active fire detection at sub-continental scaled. Then in 1990 GOES 4km high frequency active

fire detection was implemented at continental scales such as with South American biomass burning. In

the 2000s, MODIS 1km data allows for around 12 hour active fire detection and characterization at the

global scale for global fire monitoring. In 2010, VIIRS 375m data was implemented at global scales to

support landscape fire analyses such as fire growth simulations. VIIRS data filled the gaps for MODIS.

With VIIRS, there are more observations with improved small fire detection capabilities.

Landsat 8 and Sentinel-2 active fire data will supplement fire mapping and modeling applications in the

future and close the gap between strategic and tactical fire mapping. INPE/Cachoeira Paulista did a

study on small fire detection, where they burned a 3x10m area composed of firewood in a location in

Brazil which coincided with the Landsat 8 overpass. They did two fires, one in the morning (for the

Landsat overpass) and one in the afternoon (for the VIIRS overpass). This small fire was detected as

shown by the graph below.

For training and outreach, there will be a training workshop in Kruger National Park, South Africa in

August 2015. The VIIRS Active Fire website can be reached at http://viirsfire.geog.umd.edu/.

0

5000

10000

15000

20000

25000

30000

10:3011:0011:3012:0012:3013:0013:3014:0014:3015:0015:30

RadiantHeatFlux(w

.m-2)

LocalTime

LandsatOverpass

VIIRSOverpass

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Operational Products Developed by INPE Fire Monitoring Program

Presenter: Fabiano Morelli, INPE

The fire pixels database has historic data from 1998

and integrates all data received by INPE ground stations

with their own methodology for processing and other

methods used in collaboration with other researchers.

Currently, there are 8 satellites collecting around 200

images per day. Average maps are generated to show

the fire dynamics throughout the season. Anomaly

maps, such as the one shown on the right, are also

produced which represent a departure from the

monthly average indicating abnormal fire activity.

Other operational products include statistical

references about fire monitoring, current situation

information to show the latest conditions, weather

products, fire risk to show how the characteristic of

vegetation impact fires, and fogograms to show the

variations in fire risk and weather variables for the next

four days at any point in the map. Burned area

mapping is also a useful product which is generated

with Landsat and MODIS data.

Working Groups

Participants from each of the countries were split into working groups (Peru, Ecuador, and Mexico and

then Colombia and Brazil) to discuss the points and questions below. The notes from this section were

provided from the groups.

Group 1: Peru/Ecuador (Mexico participating)

Points for Discussion/Questions:

Peru/Ecuador are both in the process of conceptualizing/beginning development of incorporating NRTM

into MRV. Not required for reporting, but multiple uses, for example:

1. early detection and improved governance/transparency of institutions responsible for management

2. Feed into deforestation monitoring system (i.e. facilitate detection & then mapping?) 3. rapid response/enforcement (illegal activities) 4. resource deployment (effective, plus planning of prescribed fires) 5. fire - response 6. general forest health

Additional considerations for inclusion in design of NRT system

7. What resolution do they anticipate needing for this activity? 8. What are the spatial characteristics of the main types of illegal activities they want to include in

the NRTM (small changes, but what patterns?) 9. Choice of satellite sensor

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10. sensor expected lifetime 11. Transition plan to next-generation sensor? 12. Distribution/dissemination methods (who are the stakeholders and how do they need this

information – simple text files; JPGs of activity; ‘share-this-viewer type capability-, users cut/paste URL such as ForWarn demonstrated)

13. Review of systems available

Discussion:

Countries in the process of developing a NRTM system would benefit from access to a comparison study

of NRTM technologies that are available to assess the systems in terms of accuracy etc. – a system may

perform well visually, but is it accurate? Major elements to be included in the comparison study, which

includes a validation activity:

1. Assessment of ‘type’ of change the system identifies 2. Level of Operational readiness. ) 3. Ease of use? 4. Computational needs of the system 5. 6. Dissemination options for the results & ease of dissemination 7. Assessment of accuracy of maps by comparison to a reference sample, including levels of

omission/commission of forest loss 8. How the system operates in different forest ecosystems (for example, dry forest vs. humid) 9. How timely can the system capture change events (daily, weekly, etc) 10. Assessment of MMU of systems (aim of statistically verifying the minimum patch of forest loss

that one system can detect) Options for stratifying reference sample for use in validation could include:

1. Based on forest loss (i.e. change) 2. Based on forest loss AND ecoregions 3. Based on forest loss AND forest loss patch size (e.g., stratify by forest loss in 1-10ha, 11-20ha,

etc.) 4. Another option, focus on areas where early warning maps don’t agree, investigate this area to

understand what is causing that difference NB: the more variables included in the stratification…more complicated the stratification and analysis of

accuracy.

Peru

What they can do now? Agreement with Terra-I to but no one in MINAM currently dedicated to helping develop Terra-I Peru

Implementation of Terra-I will enable them to, for example, divide by concessions and understand the dynamics on ongoing activities per concession

Ease of use of Terra-I is v appealing

Regional groups have not used these data as yet

Peru has not looked at fires to date – so Firecast would be a good option here (Note: this is included in FireCast Phase II)

Activities like assessing general forest health are not the focus at the moment; mainly operational forest cover change is the key (ministries first need to understand forest change)

36

Ecuador

Conceptual model developed, but what would help them to go from a model to a system?

Dry forest, humid forest, Amazon in general is different in Ecuador to that of Brazil – maybe a MODIS-based system isn’t suitable here.

Smaller activities are more of a problem in Ecuador

Maybe a GUI-based system that takes out some of the analyst interaction would be suitable for Ecuador

Alternatively, there is interest in the development of a specific algorithm for use in the country Mexico

Level of usability of a NRTM system is a consideration for Mexico. Max-Mex, for example, the LCLCC system is being implemented in ministries @ national and provincial scale and this is proving problematic due to level of analyst needed

Group 2: Colombia/Brazil

Points for Discussion/Questions:

The Early Warning Systems in these countries are already in place.

Needs and improvement:

1) System improvements. Adaption of other systems/ development of new algorithms

2) Interest in additional uses of the warning system in place (e.g. fire, forest health, diseases, rates

of recovery, harvest/deforestation)

3) Frequency of rapid response, and reporting intervals

4) Data needed (ie. cloud covered areas) based on 1, 2, and 3

5) Products: binary or magnitudes

6) System to disseminate early response/enforcement

Future vision and direction of the system:

7) Transition plan to next-generation sensors (life expectancy of the sensors currently used)

8) Community involvement on reporting changes on real-time.

Discussion:

1) System improvements. Adaption of other systems/ development of new algorithms

For Brazil, IBAMA is running the systems using virtual interpretation with the purpose of not accepting

errors. The visual interpretation is the more reliable as it has fewer errors. The automatic classification

has around 80% accuracy, in the whole system with the visual interpretation the accuracy is 95%. There

is no automatic processing for detecting the deforested area; the automatization is to create a fraction

image. Upon this one the user will use interpretation.

Julio Dalge (INPE) commented that when the scientists are doing the same thing for years, there is a

traditional component. The remote sensing department and the image processing department have not

reviewed available algorithms. Re-training people is a big issue if you change the algorithm. It is a trade-

off among improving the systems and the cost of training.

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The value of having different systems like Brazil add to the challenge of adding new capabilities to the

systems, like for example adding the capacity to evaluate degradation, palm plantation, etc. In some

extends some of the systems are subsystems, like PRODES is part of the DETER System.

Gustavo Galindo (IDEAM) stated that TerraAmazon is being used for quality control, and they are

focusing in that capacity of TerraAmazon instead of using the complete system. The key is that with any

system they have to rely on the visual interpretation as the final step, and it is important to train the

people in that. Colombia is reviewing some of the systems ready in Brazil, and there are a lot of

commission errors, but with the correction the results are good. They are finding errors in the validation

that they have done with drones. They have reviewed terra-i. For Colombia it is important to be

operational at the government level, they cannot access omissions errors for operational systems. In

terra-i they were analyzing 2004 and it was a Nina event, so Colombia understood those errors, but

terra-i presented the data just as it was.

Cesar Diniz (FAO International Consultant) stated that regardless, you are going to combine visual

interpretation and automatic algorithm. There is no single algorithm that can detect change out of the

ranges such as different biomass in Brazil. The combined approach for him is a bad solution. They are

trying to segment the system (one system for type of land). The system relies on how the users are

trained.

Coordination and communication is enhancing with the different centers of INPE.

In Colombia data are generated at the national level for the regions, and in Brazil there are others

institutions that need to agree with the release of the data.

Cesar Diniz (FAO International Consultant) added that when DETER data was release in 2004, they

started seeing remote sensing and understood that there were things they were not seeing. They

understood that MODIS data has bigger pixels. One thing that has to be clear, is releasing to the general

public, not releasing to the environmental agencies. INPE release to the environmental agencies daily

since 2004 with the beginning of DETER. The presidential mandate only applies to the public. Simply put,

the general public is not aware of what is happening.

2) Interest in additional uses of the warning system in place (e.g. fire, forest health, diseases, rates of

recovery, harvest/deforestation)

Brazil: INPE does not have the needed equipment to research these areas for forest health. They can go

to the level of research for droughts. If they are going to research, they will leave the operational area.

They have a group of researchers, but they do not recognize any researchers working in an operational

area. Researchers just do not like to be committed to something operational, because of the trade-off.

Colombia: Forest health is also research. For research the opportunity is joining with universities, it is a

very small window of opportunity for IDEAM to do research and link that to operational systems.

The link is very important. The countries will learn and make it operational. The science is applied to the

benefit of the population. Early warning system is indirect with REDD. For example Colombia is starting

38

monitoring, but they do not know about the drivers – the lack is related to the fact that they are not

connected to REDD. The money is where REDD is.

Cesar Diniz (FAO International Consultant): They are starting to learn more about the forest and the

dynamics. 30% of the income is from agriculture.

3) Frequency of rapid response, and reporting intervals

Colombia is reporting the change every 6 months. The issue with reporting more frequently is clouds.

More than that, most of the early warning systems are based in MODIS, where the pixels are too coarse

to distinguish between degradation and deforestation.

4) Data needed (ie. cloud covered areas) based on 1, 2, and 3 and 7) Transition plan to next-generation

sensors (life expectancy of the sensors currently used)

For clouds they are only two solutions now for Brazil: radar or multiple uses of sensors. The price for

radar is extremely expensive for Brazil. It is cheaper to build a satellite. Colombia will have full support of

CBERS. Colombia will have exactly the same coverage for CBERS than Brazil has.

Colombia is using a Landsat base every 15 days. They were planning on using ScanSAR, but it was about

$15 per image. Planet labs is another option, however the calibration is not accurate.

5) Products: binary or magnitudes

Binary is deforestation and not deforestation. If the product is to alert for deforestation and not

deforestation, then it is binary. PRODES quantifies magnitude, with how much of the forest is lost or

gained. We must know where the alert is for with details on the percentage of the area percentage is

that used for different land uses. By law In the Amazon, if you buy an area of land, you can cut 20% of

the area.

Colombia has magnitude at this time. They only want to know where things are happening. There is

another system, which is doing the work of deforestation area.

8) Community involvement on reporting changes on real-time.

Brazil: Is not possible to include communities on DETER. Deforestation gives the communities money.

The range of employment is impacted by deforestation. There is a program in Brazil calling green

municipality, it is about how in a couple of years the communities will move out of the deforestation

model and then they can join the program and get compensation for conservation.

Gustavo Galindo (IDEAM): at the local level one of the problems is how to relate to the land tenure. The

early warning systems have to work at the two levels, alongside the communities because they are the

owners of the forest. This only works in the communities that have very good governance. About 70% of

the deforestation of the countries is concentrated in 6 Landsat images, so there is hope to center the

work there and use higher resolutions images.

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Day 5: January 23, 2015

Capacity Building Initiatives

INPE Capacity Building

Presenter: Cesar Diniz, FAO International Consultant

As a background, Cesar Diniz discussed the Amazon program, specifically focusing on PRODES and

TerraAmazon. PRODES is a system for measuring the annual rate of deforestation and the program can

be divided into three periods. In 1988 when PRODES began, processing was non-existent, so the first

period (1988-1996) was analogical. INPE used printed maps and had an overlay to draw polygons with

color pens. This overlay was then digitized with a scanner. This was a very detailed and tedious process,

which is why Brazil decided to go a different direction to make the drawing process easier, which

resulted in the Spring program during the second period (1997-2004). The Spring system was the first

digital program. The base of Spring was Landsat, and it had a database for digital image processing.

Finally, TerraAmazon started the third period (2004-Present). This put everyone together in a single

multi-user environment. It is a unified database, where there is topological control to ensure

overlapping areas and gap areas are accounted for based on control rules that are made by the user.

Difference rules and clean rules can be applied to change the way the data are displayed.

TerraAmazon allows people to work together on the same project. As shown below, individual work will

appear with green cells, while a colleague’s will appear red.

Not every user can do everything that they want, but rather they each have access based on their

position.

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There will be three units for INPE Amazon. The current unit, Belem, focuses on satellite monitoring for

the Brazilian Legal Amazon and capacity building for tropical forest monitoring at the national and

international level. Belem has two training rooms as well as a large auditorium. The second upcoming

unit, Boa Vista, will receive, process, and disseminate satellite imagery, while the third upcoming unit in

Manaus will support studies for modeling climate change.

In terms of capacity building, there are three current projects with FAO and ACTO, which focus on how

to use the programs to do visual interpretation. Currently, the training is only using Prodes as the main

example but INPE is willing to expand. The projects use the basic concepts of remote sensing, digital

image processing, and geoprocessing to allow technicians to return to their countries with a better

understanding of the process. The INPE website provides training information with the name of the

trained individual and the type of training received to avoid repetition.

The first course was in October 2010. Since then, almost 300 people have been trained on the

international level. This number greatly increases with the national level.

Questions/Comments:

Doug Muchoney (SilvaCarbon) asked about the current status of Spring. INPE responded there

they are struggling to keep maintaining Spring due to budgeting issues. INPE intends to keep

updating it and they expect to have a new version by the end of the year.

Pontus Olofsson (BU) asked if it would be helpful to get data in a global equal area projection as

it would reduce the steps in resampling. INPE responded that it would be beneficial for sure.

Gustavo Galindo (IDEAM) asked if there are modifications needed to have PRODES work in other

types of forest. Dalton Valeriano responded that the issue right now is to move out of evergreen

rainforest as the whole methodology INPE has for evergreen forest is not very translatable to

other forests. INPE is very interested in having something similar to Terra-i. The technique they

have is not directly applicable to seasonal forests. They have the funding to do this, but it is

difficult to get started.

Capacity building efforts FAO

Presenter: Inge Jonckheere, FAO Forestry Department

FAO has advantages in information systems and early warning systems through programs such as the

Global Information and Early Warning System (GIEWS), EMPRES (for hazards such as pests and diseases),

the Global Forest Fire Information Management System (GFIMS), the Global Early Warning System

(GLEWS), and UN-REDD, which has a country specific web-platform to monitor REDD+ activities. For

FAO, it is very important that these programs are simple, country specific, and open source.

From the REDD+ Decision 4/CP.15, developing countries have to measure and report on forest-related

greenhouse gas emissions in a transparent way. FAO looked at what the real issue is for countries with

deforestation and found an issue in access to satellite data. As a solution, FAO will make the satellite

data and processing tools available over the internet with the appropriate training for each country.

The Space Data Management System (SDMS) will acquire, query, process, and deliver earth observation

data and forest information products to developing countries. It is a very new program and a great

opportunity for countries.

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For the project, the two main components are training and cloud computing infrastructure. The

overarching goal is to allow developing counties to build the autonomous capacity to monitor their

forest-related REDD+ activities by guaranteeing data access and delivery of (pre)processed satellite data.

This data will allow the countries to get forest information needed to report regularly.

All the Landsat data and the data gathered from FAO will be available in the cloud and SDMS would like

to include radar in the future. Each country will have access to the cloud. All the relevant algorithms are

on there for classification, as well all the methods and tools. Countries can pick and choose what data

they will use. This is a 3 year projects that started with 4 countries in 2014 and will add 4-6 countries in

both 2015 and 2016.

There is collaboration between INPE and FAO to implement and train for national forest monitoring

systems in UN-REDD countries. These training are free and supported by analysis and programming

teams in Brazil and FAO. The training is on the software and Brazilian national forest monitoring

techniques. FAO supports the Democratic Republic of Congo, Paraguay, Ecuador, Papua New Guinea,

Zambia, Argentina, Bolivia, Peru, Congo, Cambodia, and the Pacific Islands. The image below shows a

sample of the NFMS Portal which is available in all the languages of the specific countries.

The web portal is available at www.nfms4redd.org.

FAO has learned that a few people can make a very large difference. There is a need to look at capacity

building in larger terms and provide more training. It is crucial to share data and data access and near

real-time monitoring is needed for early warning over reporting.

Questions/Comments:

Pontus Olofsson (BU) remarked on the issue of internet access throughout these developing

countries and asked how FAO is dealing with this in terms of accessing the data on the cloud.

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Inge Jonckherre responded that FAO is looking into satellite internet options for each country,

but for this part the country only has to download the data they want, rather than the Landsat

mosaics that took so much time before.

Gustavo Galindo (IDEAM) asked how the license is managed for the commercial imagery. Inge

stated that RapidEye is aware of this project and has come up with specific license restrictions.

However, everyone will be able to use the data at both the national and local level.

ALOS PALSAR 25m Global Mosaic Data

Presenter: Ake Rosenqvist, soloEO

Since October 31, 2014, JAXA has made PALSAR 25m global mosaic data openly available free of charge.

Annual mosaics from 2007- 2010 are available in 1°x1° tiles. This data can be downloaded from

http://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm. The user needs to register, but then they

can download tiles of an area of interest for certain years. The user can download forest/non forest data

or actual imagery, though Ake Rosenqvist recommends downloading the actual PALSAR imagery. The

user will then get the raw data at 4500x4500 pixels as a 16 bit GEOTIF. This data are radiometrically and

geometrically corrected for topography.

Panel Discussion A panel was held to discuss additional questions from the participants.

What would be the different levels of development for an early warning system? What would be the steps and milestones?

Pontus Olofsson (BU) answered that is depends on the aim. For example, if the country wants to

develop their own system the first step would be to make a decision on whether or not to use

something already made like Terra-I or make something new.

Brian Zutta (MINAM) stated that this was his question and clarified by saying that it would be

good to have this mapped out in a step-by-step manual. There needs to be a decision of what

those milestones are and what is needed to get there. This could be like a cookbook or as some

form of comparison.

Dalton Valeriano (INPE) commented that in Brazil early warning system are very effective

because there is an 80% chance that the change is illegal deforestation. The main purpose for

Brazil was to produce very quick information for the country to act. But some other countries

may have a different aim. Dalton saw very clever approaches for early warning here, but the

usefulness depends on the aim.

How does INPE classify or report the areas that have been regenerated to forest class since the year that began PRODES project?

Marcos Adami (INPE) responded that INPE has a project (TerraClass) that counts classes like

secondary vegetation because the regrowth needs to be classified into the different types of

vegetation that is growing.

Dalton Valeriano (INPE) added that today Brazil is very careful in deciding if burned areas are

deforestation or not. It is better to not map deforestation until the next year to see that it is

confirmed. With a time series, a country can see when an area was disturbed first. This is not

difficult, but there is a need for money and people.

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Are there plans to extend these systems to other forest countries (e.g. Tanzania, Cameroon, DR, Indonesia)

Pontus Olofsson (BU) stated that the systems are active in those countries. There will be the

same type of workshop in those countries. If they are interested they will receive support.

First I have a recommendation rather than a question: this group might want to build on the political leverage that GEO/GFOI/FAO, etc. provide to try and establish mechanisms to facilitate access to existing SAR data for the region.

Inge Jonckheere (FAO) commented that yes, it will happen from the FAO and GFOI

perspective. For GFOI, it will be crucial to have good access and they are optimistic.

Wilfrid Schroeder (UMD) who recommended this point commented on his own experience

with JAXA, who opened up its archive to those working in his project. Wilfrid recommended

that the group should start off with an attempt to get the data free of charge from

collaboration leads.

Should a regional "golden tile" be selected (e.g., a single Landsat scene in a tri-national border like Brazil-Bolivia-Peru) allowing the different countries/groups to more easily test - and most importantly - compare new methods and techniques? This could facilitate evaluation of the different approaches used by each country, therefore identifying qualities and limitations involved.

Wilfrid Schroeder (UMD) commented that this was his suggestion and that he attended a

similar workshop with this in mind. If the countries could leverage the time and energy that

they did on providing this information, a small area could be selected as a starting point. By

combining three countries, the group could take advantage of what is being done and put

the data in one database to see which approaches are going well. For the starting point, a

few groups could start testing methodologies and then export to other areas and share the

investment of the area.

Did you have viable (economic and operationally) automatic early warning systems to monitor large areas? Is it using better resolution than MODIS or is it based on radar? For the matter of enriching the discussion…Is it better to focus on development of fully automatic systems or should we focus in visual interpretation methods? What is to be considered here?

Dalton Valeriano (INPE) commented that INPE has to try as much as they can and they have to

be fast and accurate, because if the information is delivered too late it will not be relevant.

Accuracy is something that should be taken into consideration and it is better to omit than

commit. The country should also abandoned any automatic approach to guarantee a level of

accuracy that will not cost as much.

Gustavo Galindo (IDEAM) stated that early warning is very important as well as the effects it will

have. In Colombia, some change was detected and alerts were sent out, which resulted in the

media reporting. This area of change was only detecting logging, and they had to do a visual

correction to take out these areas.

Cesar Diniz (FAO Consultant) stated that it is very dependent on the country and it will come to

whether or not the country has a high level algorithm capability. If so, the country should

combine this information. If the capacity is not there, then it is better to spend more time on

basics remote sensing.

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It is interesting to explore how capacity building and knowledge transfer may take place without interfering with each country sovereignty….thoughts?

Inge Jonckheere (FAO) stated that it is true that this is a very delicate point and that before FAO

it should serve the needs of the countries. FAO is not there to say what a country is doing is

wrong but rather to say there is another option. FAO is just offering support, but there is

definitely a delicate balance.

Sylvia Wilson (SilvaCarbon) commented that sometimes the decisions in implementing these

tools are developed at the top level, such as from the donors who think it will be helpful, but a

lot of times the countries were never even asked if it would be helpful.

Dalton Valeriano (INPE) stated that INPE has dealt with the easy part so far in how to deal with

evergreen forest. INPE needs to think about how to deal with long time series of Landsat data.

They are already thinking about some limitation in terms of memory space for this data. There

should be a dialogue between GEO and Google on getting each country access to their own

imagery. Otherwise the countries will just repeat what Google has already done.

o Wilfrid Schroeder (UMD) stated that he went to a meeting in Colorado with Rebecca

Moore (Google). Google has a really aggressive agenda for collecting, archiving, and

delivering data. The group needs to be careful in how they negotiate with Google, as it

could end either really well or poorly.

Could FAO include a firecast warning system? Could GFOI offer training in design and implementation in algorithms?

Inge Jonckheere (FAO) supposed that this could be done. There is a budget for training, but

these are REDD countries.

Sylvia Wilson (SilvaCarbon) added that SilvaCarbon and FAO could provide this training. It is not

impossible, but rather a matter of interest based on the countries.

Is it possible to combine many methodologies for the same region? More information about the systems already developed (the way they work), and how they compare to each other. Advantages and disadvantages.

Pontus Olofsson (BU) commented that they would need information if they wanted to combine

methodologies.

Oscar Bautista (Terra-i) stated that with different methodologies, the result is the result of those

methods. It is important to measure in the same way to later compare and have an accurate

number. The most important is the result rather than the methodology.

Julio Dalge (INPE) added that if you try to compare the numbers it is too complex. When there is

an operational system in place, there is not much room to accommodate many methodologies

because in the end they have to give a very robust set of number to the government.

Dalton Valeriano (INPE) added that everything has to be consistent, and once a country decides

on one method they must stick with it for a long time, otherwise the data are not reliable.

Oscar Bautista also added that it might be useful to compare the results between the countries.

Gustavo Galindo (IDEAM) stated that it would be beneficial to compare, as it is a richer result to

compare different methods. It is important there is an official result, and it can be mixed which

is the complicated part.

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I think it would be useful to conduct a study comparing the different EWS products that are available. Such a study would include measures of performance, cost, ease of use, ease of implementation, computing resources needed, etc. This would be highly valuable for countries looking to implement or improve their EWS capabilities. Maybe SilvaCarbon should fund and initiate such a study.

Pontus Olofsson (BU) stated that for countries like Peru and Ecuador it would be helpful to make

that type of decision if they have a comparison study.

Sylvia Wilson (SilvaCarbon) added that for the SilvaCarbon program it is not impossible but

depends on how efficient it will be.

In terms of traceability, be part of the SDMS would let the negotiations of REDD+ be more transparent and easily?

Inge Jonckheere (FAO) answered that if it is the data download then no. If it is for reporting,

then yes. It is about how the statistics are created, it is good to have open algorithms. For sure it

would help country to reprocess.

Closing

The 12th Regional Workshop was closed with a thank you for all to attending from Dalton Valeriano. INPE

was very pleased to host this meeting and hope to be a part of future workshops. Sylvia Wilson closed

with a special thanks from SilvaCarbon to INPE for hosting the event.

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List of Participants:

Name Institution Email

1 Nestor Acosta MAE- Ecuador nestor.acosta@ambiente.gob.ec

3 Marcos Adami INPE -Brazil adami16@gmail.com

4 Marcia Alvarenga INPE - Brazil marcia@dsr.inpe.br

5 Oscar Bautista CIAT - Colombia o.bautista@cgiar.org

6 Andrea Bustos Ministry of Environment - Ecuador andrea.bustos@ambiente.gob.ec

7 Chelsea Cook USGS - US cjcook@usgs.gov

8 Julio Dalge INPE - Brazil Julio@dpi.inpe.br

9 Cesar Diniz INPE - Brazil cesar.diniz@inpe.br

10 Leila Fonseca INPE - Brazil leila@dpi.inpe.br

11 Gustavo Galindo IDEAM - Colombia ggalindo@ideam.gov.co

12 Eliakim Hamunyela Wageningen University - The Netherlands eliakim.hamunyela@wur.nl

13 William Hargrove U.S. Forest Service whargrove@fs.fed.us

14 Ximena Herrera Ministry of Environment - Ecuador ximena.herrera@ambiente.gob.ec

15 Jennifer Hewson Conservation International - US jhewson@conservation.org

16 Inge Jonckheere FAO - Italy inge.jonckheere@fao.org

17 Fabiano Morelli INPE - Brazil fabiano.morelli@cptec.inpe.br

18 Doug Muchoney USGS - US dmuchoney@usgs.gov

19 Igor Narvaes Brazil igor.narvaez@inpe.br

20 Pontus Olofsson Boston University - US olofsson@bu.edu

21 Alicia Peduzzi U.S. Forest Service apeduzzi@fs.fed.us

22 Raul Rodriguez Mexico raul.rodriguez@conafor.gob.mx

23 Ake Rosenqvist JAXA - Japan ake.rosenqvist@soloeo.com

24 Guillermo Sanchez U.S. Forest Service - Ecuador gsanchez.usfs@gmail.com

25 Wilfrid Schroeder NOAA-US wilfrid.schroeder@noaa.gov

26 Dalton Valeriano INPE - Brazil dalton@dsr.inpe.br

27 Sergio Villela CONAFOR - Mexico svillela@conafor.gob.mx

28 Sylvia Wilson USGS - US snwilson@usgs.gov

29 Brian Zutta MINAM - Peru bzutta@minam.gob.pe

Report by: Chelsea Cook