2019 CEOS Chair Initiative
As 2019 CEOS Chair, VAST-VNSC is leading an initiative to integrate a number of ongoing CEOS activities, data, algorithms, and training/capacity building in support of forest and rice crop monitoring for the Mekong Delta region.
Over the last few years, CEOS Agencies have prioritised efforts to make EO data more accessible, through initiatives such as Analysis Ready Data, Open Data Cube, and the Copernicus Data Information Access Services (DIAS).
As the CEOS Chair for 2019, Vietnam seeks to integrate many of these ongoing CEOS activities in support of regions most affected by climate change, with a focus on the tropical monsoon climate countries in South East Asia (Cambodia, Lao PDR, Thailand, and Vietnam).
By putting the focus on this specific region, the CEOS Chair hopes to enhance the contribution and cooperation of CEOS Agencies in the region, identify new Earth observation users, and to respond effectively to their needs through integration across the full range of Earth observations by promoting the sharing of CEOS Agency data and algorithms, and by improving access to and the use of such data via modern data architectures. Integrating existing CEOS thematic acquisition strategies and making data and algorithms available to the international community will enable studies and applications in relation to forests, agriculture, disasters, climate, carbon, and water.
The 2019 Chair Initiative concept is one of a regional observatory, which is intended to continue beyond 2019 and also be extended to other hotspot regions.
As the CEOS Chair for 2019, Vietnam seeks to integrate many of these ongoing CEOS activities in support of regions most affected by climate change, with a focus on the tropical monsoon climate countries in South East Asia (Cambodia, Lao PDR, Thailand, and Vietnam).
By putting the focus on this specific region, the CEOS Chair hopes to enhance the contribution and cooperation of CEOS Agencies in the region, identify new Earth observation users, and to respond effectively to their needs through integration across the full range of Earth observations by promoting the sharing of CEOS Agency data and algorithms, and by improving access to and the use of such data via modern data architectures. Integrating existing CEOS thematic acquisition strategies and making data and algorithms available to the international community will enable studies and applications in relation to forests, agriculture, disasters, climate, carbon, and water.
The 2019 Chair Initiative concept is one of a regional observatory, which is intended to continue beyond 2019 and also be extended to other hotspot regions.
Motivation
This project seeks to integrate a number of ongoing CEOS activities and data in support of forest and rice crop monitoring for the Mekong Delta region, and will:- Support an engaged user base;
- Build a regional observatory to produce tangible outcomes with enduring benefits;
- Contribute to CEOS and also build domestic capacity beyond 2019;
- Demonstrate the utility of each component, and also what can be achieved through their integration.
Open Data Cube
The project will leverage and expand the Open Data Cube implemented at VAST-VNSC in Hanoi, Vietnam, to users in the Mekong Delta region.
The Open Data Cube (ODC) provides an integrated gridded data analysis environment for decades of analysis ready Earth observation data from multiple satellite and other acquisition systems. ODC is an open and freely accessible exploitation architecture.
CEOS Analysis Ready Data
CEOS Analysis Ready Data for Land (CARD4L) is critical for the population of the Data Cube and other future data architectures.CARD4L are satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets.
Rice Products
Rice Maps
One of the key variables in the development of a monitoring strategy for Asian rice crops is the location and timing of crops across the region. Crop area maps are the basis for secondary products such as yield estimates and support the development of acquisition strategies and ground validation work.
Contributors:
End users: Vietnam Ministry of Agriculture and Rural Development, Mekong River Commission
Data used: Sentinel-1, Sentinel-2, Landsat 7, Landsat 8, ALOS-2
Algorithms: Being developed by L. D. Nguyen + GEORice algorithms
 |
European Space Agency (Sentinel data) |
 |
European Commission (Sentinel data) |
 |
United States Geological Survey (Landsat data) |
 |
Japan Aerospace Exploration Agency (ALOS-2 data, INAHOR Rice / Non Rice Maps) |
  |
CESBIO/CNES (data, processing, and algorithms) |
 |
GEORice (ground data collection, algorithms, comparison products) |
End users: Vietnam Ministry of Agriculture and Rural Development, Mekong River Commission
Data used: Sentinel-1, Sentinel-2, Landsat 7, Landsat 8, ALOS-2
Algorithms: Being developed by L. D. Nguyen + GEORice algorithms
Rice Phenology / Growth Stage
The typical growth cycle for Asian rice crops is 85-150 days, and many rice growing regions produce two or three crops a year. There is a lot of variation between cropping areas. During a typical 120 day growth cycle, 4-5 satellite observations are generally required in order to achieve all monitoring objectives, and it is especially important to have observations at sowing and harvest times.
Contributors:
End users: Vietnam Ministry of Agriculture and Rural Development, Mekong River Commission
Data used: Sentinel-1, Sentinel-2, Landsat 7, Landsat 8, ALOS-2
Algorithms: Being developed by L. D. Nguyen + GEORice algorithms
|
European Space Agency (Sentinel data) |
|
European Commission (Sentinel data) |
|
United States Geological Survey (Landsat data) |
|
Japan Aerospace Exploration Agency (ALOS-2 data, INAHOR Rice / Non Rice Maps) |
|
CESBIO/CNES (data, processing, and algorithms) |
|
GEORice (ground data collection, algorithms, comparison products) |
End users: Vietnam Ministry of Agriculture and Rural Development, Mekong River Commission
Data used: Sentinel-1, Sentinel-2, Landsat 7, Landsat 8, ALOS-2
Algorithms: Being developed by L. D. Nguyen + GEORice algorithms
Rice Crop Production/Yield Estimation
World population, and therefore demand for food, has increased linearly over the last fifty years (+80M/year), and is projected to keep growing until around 2050 up to 9 billion inhabitants (United Nations Department of Economic and Social Affairs, Population Division; 2004). This conjuncture is prone to create tensions in food markets that could lead to world food price crises - as in 2008 when the price of rice more than doubled in only seven months - and eventually to famines. In this context of price instability and threatened food security, tools to monitor rice production in real-time are highly needed by governments, traders, and decision makers.Yield estimation for rice crops is not yet fully implemented with EO, and model verification is required.
Contributors:
End users: Vietnam Ministry of Agriculture and Rural Development, Mekong River Commission
Data used: Sentinel-1, Sentinel-2, Landsat 7, Landsat 8, ALOS-2
Algorithms: Being developed by L. D. Nguyen + GEORice algorithms
|
European Space Agency (Sentinel data) |
|
European Commission (Sentinel data) |
|
United States Geological Survey (Landsat data) |
|
Japan Aerospace Exploration Agency (ALOS-2 data, INAHOR Rice / Non Rice Maps) |
|
CESBIO/CNES (data, processing, and algorithms) |
|
GEORice (ground data collection, algorithms, comparison products) |
End users: Vietnam Ministry of Agriculture and Rural Development, Mekong River Commission
Data used: Sentinel-1, Sentinel-2, Landsat 7, Landsat 8, ALOS-2
Algorithms: Being developed by L. D. Nguyen + GEORice algorithms
Forest Products
Forest Maps
Forests play an important role in the tropical countries of the Mekong region. While there are forest maps for each country, there is no common map classification system, and the forest maps in each country are updated in different phases. Therefore, joint management efforts related to forest information are challenging.Objective: Simplify mapping of forests from satellite imagery for the purposes of forest monitoring in the Mekong River region, as well as towards the requirements of the REDD+ program.
Contributors:
End users: Mekong River Commission, Vietnam Ministry of Agriculture and Rural Development
Data used: Sentinel-1, Sentinel-2, Landsat 7, Landsat 8
Algorithms used: Being developed by VNSC and FIPI
|
European Space Agency (Sentinel data) |
|
United States Geological Survey (Landsat data) |
|
CESBIO/CNES (data processing and algorithms) |
 |
Vietnam Forest Inventory and Planning Institute (validation data and field data) |
End users: Mekong River Commission, Vietnam Ministry of Agriculture and Rural Development
Data used: Sentinel-1, Sentinel-2, Landsat 7, Landsat 8
Algorithms used: Being developed by VNSC and FIPI
Forest Change Monitoring
Rapid forest changes are occurring in the Mekong region. The forest changes, including deforestation and reforestation are a consequence of parallel processes: forest fires; economic development activities such as timber extraction, the expansion of agricultural land, coffee and rubber plantation; natural regeneration from abandoned areas; and government forestation efforts. Regular forest monitoring from satellite data helps to identify deforestation hotspots and provide timely information for forest management.
Contributors:
End users: Mekong River Commission, Vietnam Ministry of Agriculture and Rural Development
Data used: Sentinel-1, Sentinel-2, ALOS-2
Algorithms used: S-1 CESBIO algorithm, ALOS CESBIO Algorithm, JAXA ALOS-2 forest algorithm
|
European Space Agency (Sentinel data) |
|
Japan Aerospace Exploration Agency (ALOS-2 data & forest algorithm) |
|
CESBIO/CNES (data processing and algorithms) |
|
Vietnam Forest Inventory and Planning Institute (validation data and field data) |
End users: Mekong River Commission, Vietnam Ministry of Agriculture and Rural Development
Data used: Sentinel-1, Sentinel-2, ALOS-2
Algorithms used: S-1 CESBIO algorithm, ALOS CESBIO Algorithm, JAXA ALOS-2 forest algorithm