PUBLICATIONS

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Image of Rice Area and Production Estimates for the 2023 Summer Season

Rice Area and Production Estimates for the 2023 Summer Season

27 October 2023 04 Author(s): Asian Disaster Preparedness Center



Image of Rice Area and Production Estimates for the 2022 Monsoon Season

Rice Area and Production Estimates for the 2022 Monsoon Season

05 May 2023 04 Author(s): Asian Disaster Preparedness Center
This report provides an in-depth analysis of unbiased rice area estimations for the major monsoon rice-producing regions of Ayeyarwady, Bago, Kayah, Kayin, Magway, Mandalay, Mon, Rakhine, Sagaing, Shan, and Yangon in 2022. The ADPC team utilized satellite image classification and high-resolution satellite data interpretation to estimate total cultivated rice area, accounting for the associated uncertainty. The team combined this area estimation with yield information from the International Food Policy Research Institute (IFPRI), which was collected via telephone surveys across various regions in Myanmar. The report reveals that Myanmar's total cultivated rice area decreased by 7 percent from 4,775,976 ha in 2021 to 4,421,860 ± 311,955 ha in 2022 across the eleven regions analyzed. The total rice production declined by 13 percent, from 14,381 thousand tons in 2021 to 12,567 ± 851 thousand tons in 2022. Developed by the Asian Disaster Preparedness Center (ADPC) and Spatial Informatics Group (SIG), this study was funded by the United States Agency for International Development (USAID) through the USAID-funded Transparency and Inclusive Growth Activity (TIGA) project, implemented by Nathan Associates.
This report provides an in-depth analysis of unbiased rice area estimations for the major monsoon rice-producing regions of Ayeyarwady, Bago, Kayah, Kayin, Magway, Mandalay, Mon, Rakhine, Sagaing, Shan, and Yangon in 2022. The ADPC team utilized satellite image classification and high-resolution satellite data interpretation to estimate total cultivated rice area, accounting for the associated uncertainty. The team combined this area estimation with yield information from the International Food Policy Research Institute (IFPRI), which was collected via telephone surveys across various regions in Myanmar. The report reveals that Myanmar's total cultivated rice area decreased by 7 percent from 4,775,976 ha in 2021 to 4,421,860 ± 311,955 ha in 2022 across the eleven regions analyzed. The total rice production declined by 13 percent, from 14,381 thousand tons in 2021 to 12,567 ± 851 thousand tons in 2022. Developed by the Asian Disaster Preparedness Center (ADPC) and Spatial Informatics Group (SIG), this study was funded by the United States Agency for International Development (USAID) through the USAID-funded Transparency and Inclusive Growth Activity (TIGA) project, implemented by Nathan Associates.



Image of Impressions: Connecting Space to Village in Southeast Asia

Impressions: Connecting Space to Village in Southeast Asia

24 January 2023 04 Author(s): ADPC



Image of Rice Area and Production Estimates for the 2021 Post-monsoon Season

Rice Area and Production Estimates for the 2021 Post-monsoon Season

30 September 2022 04 Author(s): Asian Disaster Preparedness Center (ADPC)
The study analyzes rice cultivation in Myanmar for the 2021 post-monsoon season, covering eight of the country's most significant rice-producing regions. The research provides a comprehensive analysis of vegetation and climate conditions in these areas and delivers accurate area and production estimates. The report draws on satellite-based remote sensing data to determine rice area including any uncertainties. This information is invaluable for the development of country-wide crop production estimates, which play a crucial role in ensuring food security and shaping future policy. The eight regions studied are Sagaing, Bago, Magway, Mandalay, Yangon, Ayeyarwady, Mon, and Shan State. Developed by the Asian Disaster Preparedness Center (ADPC) and Spatial Informatics Group (SIG), this study was funded by the United States Agency for International Development (USAID) through the USAID-funded Transparency and Inclusive Growth Activity (TIGA) project, implemented by Nathan Associates.
The study analyzes rice cultivation in Myanmar for the 2021 post-monsoon season, covering eight of the country's most significant rice-producing regions. The research provides a comprehensive analysis of vegetation and climate conditions in these areas and delivers accurate area and production estimates. The report draws on satellite-based remote sensing data to determine rice area including any uncertainties. This information is invaluable for the development of country-wide crop production estimates, which play a crucial role in ensuring food security and shaping future policy. The eight regions studied are Sagaing, Bago, Magway, Mandalay, Yangon, Ayeyarwady, Mon, and Shan State. Developed by the Asian Disaster Preparedness Center (ADPC) and Spatial Informatics Group (SIG), this study was funded by the United States Agency for International Development (USAID) through the USAID-funded Transparency and Inclusive Growth Activity (TIGA) project, implemented by Nathan Associates.



Image of Rice Map and Area Estimates of Rice Cultivation in Myanmar in the Monsoon Season of 2021

Rice Map and Area Estimates of Rice Cultivation in Myanmar in the Monsoon Season of 2021

28 September 2022 04 Author(s): ADPC/SERVIR-Mekong and Spatial Informatics Group (SIG)



Image of Earth Science Applications Guidebook

Earth Science Applications Guidebook

13 April 2022 04 Author(s): -



Image of SERVIR-Mekong Factsheet

SERVIR-Mekong Factsheet

04 March 2022 04 Author(s): SERVIR-Mekong



Image of Commodity-Driven Forest Loss: A Study of Southeast Asia

Commodity-Driven Forest Loss: A Study of Southeast Asia

05 February 2021 04 Author(s): U.S. Department of Agriculture’s Forest Service (USFS), Spatial Informatics Group (SIG), World Agroforestry Centre (ICRAF), and SERVIR-Mekong



Image of Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities

Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities

11 June 2020 04 Author(s): David Saah, Karis Tenneson, Mir Matin, Kabir Uddin, Peter Cutter, Ate Poortinga, Quyen H. Nguyen, Matthew Patterson, Gary Johnson, Kel Markert, Africa Flores, Eric Anderson, Amanda Weigel, Walter L. Ellenberg, Radhika Bhargava et al.
Land cover maps are a critical component to make informed policy, development, planning, and resource management decisions. However, technical, capacity, and institutional challenges inhibit the creation of consistent and relevant land cover maps for use in developing regions. Many developing regions lack coordinated capacity, infrastructure, and technologies to produce a robust land cover monitoring system that meets land management needs. Local capacity may be replaced by external consultants or methods which lack long-term sustainability. In this study, we characterize and respond to the key land cover mapping gaps and challenges encountered in the Lower Mekong (LMR) and Hindu Kush-Himalaya (HKH) region through a needs assessment exercise and a collaborative system design. Needs were assessed using multiple approaches, including focus groups, user engagement workshops, and online surveys. Efforts to understand existing limitations and stakeholder needs resulted in a co-developed and modular land cover monitoring system which utilizes state-of-the-art cloud computing and machine learning which leverages freely available Earth observations. This approach meets the needs of diverse actors and is a model for transnational cooperation. Read full article here 
Land cover maps are a critical component to make informed policy, development, planning, and resource management decisions. However, technical, capacity, and institutional challenges inhibit the creation of consistent and relevant land cover maps for use in developing regions. Many developing regions lack coordinated capacity, infrastructure, and technologies to produce a robust land cover monitoring system that meets land management needs. Local capacity may be replaced by external consultants or methods which lack long-term sustainability. In this study, we characterize and respond to the key land cover mapping gaps and challenges encountered in the Lower Mekong (LMR) and Hindu Kush-Himalaya (HKH) region through a needs assessment exercise and a collaborative system design. Needs were assessed using multiple approaches, including focus groups, user engagement workshops, and online surveys. Efforts to understand existing limitations and stakeholder needs resulted in a co-developed and modular land cover monitoring system which utilizes state-of-the-art cloud computing and machine learning which leverages freely available Earth observations. This approach meets the needs of diverse actors and is a model for transnational cooperation. Read full article here 



Image of Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000‐2017 Landsat time-series

Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000‐2017 Landsat time-series

11 June 2020 04 Author(s): P. Potapova, A. Tyukavinaa, S. Turubanova, Y. Talero, A. Hernandez-Serna, M.C. Hansen, D. Saah, K. Tenneson, A. Poortinga, A. Aekakkararungroj, F. Chishtied, P. Towashiraporn, B. Bhandarid, K.S. Aungd, Q.H. Nguyen
Spatially and temporally consistent vegetation structure time-series have great potential to improve the capacity for national land cover monitoring, to reduce latency and cost of international reporting, and to harmonize regional land cover characterizations. Here we present a semi-automatic, operational algorithm for mapping and monitoring of woody vegetation canopy cover and height at a regional scale using freely available Landsat timeseries data. The presented algorithm employs automatic data processing and mapping using a set of lidar-based vegetation structure prediction models. Changes in vegetation cover are detected separately and integrated into the structure time-series. Sample-based validation and inter-comparison with existing datasets demonstrates the spatial and temporal consistency of our regional data time-series. The dataset reliably reflects changes in tree cover (tree cover loss user's accuracy of 0.84 and producer's accuracy of 0.75) and can serve as a tool to map annual forest extent (user's accuracy of 0.98 and producer's accuracy of 0.81 for 10% canopy cover threshold to define the forest class). The tree height estimates are consistent with a GLAS-based global map (mean average error of 3.7 m, the correlation coefficient of 0.92 and the R2 of 0.85). The algorithm was prototyped within the Lower Mekong region where it revealed an intensive woody vegetation dynamic. Of the year 2000 forest area (defined using canopy cover threshold of 10% and tree height threshold of 5 m), 9.4% was deforested by the year 2017, and 16.6% was affected by stand-replacement disturbance followed by reforestation. The average annual area of stand-level forest disturbance within the region was 2.34 Mha, and increased by 34% from 2001 (1.85 Mha) to 2017 (2.48 Mha). Total forest area decreased by 6.2% within the region, and 11.1% of year 2000 primary forest area was lost by 2017. At the national level, Cambodia demonstrated the highest rate of deforestation, with a net forest area loss of 22.5%. We estimated that 21.3% of 2017 forest cover had an age of 17 years or less, illustrating the intensive forest land uses within the region. The time-series product is suitable for mapping annual land cover and inter-annual land cover change using customized class definitions.
Spatially and temporally consistent vegetation structure time-series have great potential to improve the capacity for national land cover monitoring, to reduce latency and cost of international reporting, and to harmonize regional land cover characterizations. Here we present a semi-automatic, operational algorithm for mapping and monitoring of woody vegetation canopy cover and height at a regional scale using freely available Landsat timeseries data. The presented algorithm employs automatic data processing and mapping using a set of lidar-based vegetation structure prediction models. Changes in vegetation cover are detected separately and integrated into the structure time-series. Sample-based validation and inter-comparison with existing datasets demonstrates the spatial and temporal consistency of our regional data time-series. The dataset reliably reflects changes in tree cover (tree cover loss user's accuracy of 0.84 and producer's accuracy of 0.75) and can serve as a tool to map annual forest extent (user's accuracy of 0.98 and producer's accuracy of 0.81 for 10% canopy cover threshold to define the forest class). The tree height estimates are consistent with a GLAS-based global map (mean average error of 3.7 m, the correlation coefficient of 0.92 and the R2 of 0.85). The algorithm was prototyped within the Lower Mekong region where it revealed an intensive woody vegetation dynamic. Of the year 2000 forest area (defined using canopy cover threshold of 10% and tree height threshold of 5 m), 9.4% was deforested by the year 2017, and 16.6% was affected by stand-replacement disturbance followed by reforestation. The average annual area of stand-level forest disturbance within the region was 2.34 Mha, and increased by 34% from 2001 (1.85 Mha) to 2017 (2.48 Mha). Total forest area decreased by 6.2% within the region, and 11.1% of year 2000 primary forest area was lost by 2017. At the national level, Cambodia demonstrated the highest rate of deforestation, with a net forest area loss of 22.5%. We estimated that 21.3% of 2017 forest cover had an age of 17 years or less, illustrating the intensive forest land uses within the region. The time-series product is suitable for mapping annual land cover and inter-annual land cover change using customized class definitions.



Image of Linking Earth Observations for Assessing the Food Security Situation in Vietnam: A Landscape Approach

Linking Earth Observations for Assessing the Food Security Situation in Vietnam: A Landscape Approach

11 June 2020 04 Author(s): Ate Poortinga, Quyen Nguyen, Karis Tenneson, Austin Troy, David Saah, Biplov Bhandari, Walter L. Ellenburg, Aekkapol Aekakkararungroj, Lan Ha, Hai Pham, Giang Nguyen, and Farrukh Chishtie
Land cover change and its impact on food security is a topic that has major implications for development in population-dense Southeast Asia. The main drivers of forest loss include the expansion of agriculture and plantation estates, growth of urban centers, extraction of natural resources, and water infrastructure development. The design and implementation of appropriate land use policies requires accurate and timely information on land cover dynamics to account for potential political, economical, and agricultural consequences. Therefore, SERVIR-Mekong led the collaborative development of a Regional Land Cover Monitoring System (RLCMS) with key regional stakeholders across the greater Mekong region. Through this effort, a modular system was used to create yearly land covermaps for the period 1988–2017. In this study, we compared this 30-year land cover time-series with Vietnam national forest resources and agricultural productivity statistics. We used remote sensing-derived land cover products to quantify landscapechanges and linked those with food availability, one of food security dimension, from a landscape approach perspective.We found that agricultural production has soared while the coverage of agricultural areas has remained relatively stable. Land cover change dynamics coincide with important legislation regarding environmental management and sustainable development strategies in Vietnam. Our findings indicate that Vietnam has made major steps toward improving its’ food security. We demonstrate that RLCMS is a valuable tool for evaluating the relationship between policies and their impacts on food security, ecosystem services and natural capital.
Land cover change and its impact on food security is a topic that has major implications for development in population-dense Southeast Asia. The main drivers of forest loss include the expansion of agriculture and plantation estates, growth of urban centers, extraction of natural resources, and water infrastructure development. The design and implementation of appropriate land use policies requires accurate and timely information on land cover dynamics to account for potential political, economical, and agricultural consequences. Therefore, SERVIR-Mekong led the collaborative development of a Regional Land Cover Monitoring System (RLCMS) with key regional stakeholders across the greater Mekong region. Through this effort, a modular system was used to create yearly land covermaps for the period 1988–2017. In this study, we compared this 30-year land cover time-series with Vietnam national forest resources and agricultural productivity statistics. We used remote sensing-derived land cover products to quantify landscapechanges and linked those with food availability, one of food security dimension, from a landscape approach perspective.We found that agricultural production has soared while the coverage of agricultural areas has remained relatively stable. Land cover change dynamics coincide with important legislation regarding environmental management and sustainable development strategies in Vietnam. Our findings indicate that Vietnam has made major steps toward improving its’ food security. We demonstrate that RLCMS is a valuable tool for evaluating the relationship between policies and their impacts on food security, ecosystem services and natural capital.



Image of Collect Earth: An online tool for systematic reference data collection in land cover and use applications

Collect Earth: An online tool for systematic reference data collection in land cover and use applications

11 June 2020 04 Author(s): David Saah, Gary Johnson, Billy Ashmall, Githika Tondapu, Karis Tenneson, Matt Patterson, Ate Poortinga, Kel Markert, Nguyen Hanh Quyen, Khun San Aung, Lena Schlichting, Mir Matin, Kabir Uddin, Raja Ram Aryal, John Dilger, Water Lee, Ellenburg et al.