GEO GFOI 19-23, 2015 INPE

Transcription

1 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.

2 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 Early Warning Systems Mexico Status and Plans for Implementation of an Early Warning System Ecuador EWS Methodologies Near Real-Time Mapping of Forest Disturbances Two MODIS-Based Approaches for Monitoring Forest Change in Near Real-Time Continuous Monitoring of Forest Change in Near Real-Time with Data from the MODIS Sensors Near Real-Time Monitoring of Land Cover Disturbance by Fusion of MODIS and Landsat Data ForWARN: A Cross-Cutting Forest Resource Management and Decision-Support System Forest Change Assessment Viewer Terra-i, A Near-Real Time Monitoring of Habitat Change INDICAR Early warning capacity by Synthetic Aperture Radar (SAR) Early Warning Systems for Fires Firecast- Fire & Forest Monitoring & Forecasting System The Global Early Warning System for Wildland Fire Global Observation on Forest and Land Cover Dynamics GOFC-GOLD Use of Active Fire Data Sets in Support of Fire Monitoring, Management and Planning Working Groups Capacity Building Initiatives INPE Capacity Building Capacity building efforts FAO ALOS PALSAR 25m Global Mosaic Data Panel Discussion Closing

3 Day 1: January 19, 2015 Welcome and Introductions The 12 th 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. 3

4 3. Methods and Guidance Documentation (Australia) a. The Spanish version of this document will be available in February 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 4

5 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. 5

6 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 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 geodatabase. The framework for the system is shown below. 6

7 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. 7

8 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 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 8

9 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 9

10 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. 10

11 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 Since 2010, this number has increased to 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 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. 11

12 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 interinstitutional 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 CONAFOR (National Forestry Commission) INEGI (National Institute of Geography and Statistics) CONABIO (National Commission for the Use and Knowledge of Biodiversity) Responsibility Generates land cover change reports Provides national regulations for cartographic products and geospatial data 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 The other objectives are to generate the historical deforestation rate for , 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. 12

13 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 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 13

14 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 The deforestation rate was also calculated between two periods: and , 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. 14

15 For the forest national inventory, both carbon and base area maps were developed from 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. 15

16 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. 16

17 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) 17

18 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. 18

19 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. Image from Xin et al. (2013). 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. 19

20 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 20

21 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/nearinfrared. 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 21

22 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. 22

23 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 23

24 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 nonhistorically 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: 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. 24

25 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 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 25

26 thrive and decline of the forests in Western North Caroline between 2000 and There is nearly no evergreen thrive present. Slides available at: 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. 26

27 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). 27

28 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. 28

29 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. 29

30 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 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 cloudprone areas. 30

31 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 realtime 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 Users can subscribe to this free service to receive 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. 31

32 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 32

33 Radiant Heat Flux (w.m -2 ) 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 Landsat Overpass VIIRS Overpass :30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 Local Time For training and outreach, there will be a training workshop in Kruger National Park, South Africa in August The VIIRS Active Fire website can be reached at 33

34 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 34

35 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 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) 35

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 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 tradeoff among improving the systems and the cost of training. 36

37 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 37

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. 38

39 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 ( ) 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 ( ). 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. 39

40 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 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. 40

41 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 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 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. 41