12 th 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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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
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
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)
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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
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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.
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.