Thanapon Piman

Systematic wetlands monitoring is crucial for informed management, timely conservations actions and consistent temporal reporting. Recent global wetland assessments show that Southeast Asia is losing wetlands at faster than rest of world, with the floodplain wetlands declining by about 1.2% per year, primarily due to agricultural conversion. Thus, making wetlands monitoring and reporting crucial. The national wetland inventory of Thailand, last complied in 2002, requires an update. In this context, the present study introduces a satellite-based Wetland Monitoring Tool (WMT) for assessing five freshwater Ramsar wetlands in Thailand.The WMT integrates multispectral Sentinel-2 and Synthetic Aperture Radar (SAR) Sentinel-1 observations to classify wetlands during the pre-monsoon period (March to May) from 2019 to 2025 in Google Earth Engine (GEE). Spectral indices (NDWI, MNDWI, NDVI, NDMI, NBR, AWEI, and Tasseled Cap components) are derived to identify pure pixels using Otsu thresholding. Sentinel-1 observations are used to complement optical observation results by improving identification of inundated and flooded vegetation, particularly in scenarios characterized by denser canopy cover or under cloud interference. Final wetland classification is done using a harmonized pixel-based, rule-based framework with an adaptive water detection approach for enhanced class separation across heterogeneous wetland ecosystems. Classification performance is evaluated using Overall Accuracy (OA), Producer’s Accuracy (PA), and the Kappa coefficient.Results show vegetation increased across all five Ramsar wetlands, especially in Khao Sam Roi Yot (+823 ha) and Lower Songhkram River Wetland (+641 ha). Four of the five wetlands showed decline in open water, with Bung Khong Long Non-Hunting Area losing maximum area ( -163.4 ha; -13%) and Nong Bong Kai Non-hunting Area losing largest proportional area (-67.4 ha; -28%). Only Khao Sam Roi Yot shows increase in open water (+159.4 ha; 6.5%). Emergent and flooded vegetation declined significantly in smaller wetlands, especially in Nong Bong Kai, where they declined 86.2% and 73.4%, respectively. In contrast, land class declined significantly in larger wetlands, particularly in Khao Sam Roi Yot (−1,141.8 ha; -28.4%) and Lower Songkhram (−534.7 ha; −9.8%), indicating rise in wetland vegetation classes within Ramsar boundaries. The integrated optical-SAR approach in WMT enhances wetlands classification, can be scaled at national and regional level and demonstrates potential for standardized, long-term mapping and reporting for improved wetland management and decision making.Keywords: Wetlands, Ramsar, Sentinel-1, Sentinel-2, Synthetic Aperture Radar, Google Earth Engine, Thailand

Wetlands contribute to human well-being in multiple ways. These contributions take various forms, including goods such as food and other raw materials, but also through services like carbon sequestration, flood and climate regulation. Despite their importance, wetlands are being lost at annual rates exceeding 0.5% of their global extent over the past sixty years, mainly due to conversion to other land uses. Several multilateral agendas emphasize the importance of wetland monitoring and protection; however, progress toward established targets remains limited by the lack of consistent national datasets and operational monitoring tools. Satellite observations provide globally consistent data that enable systematic wetland monitoring. In particular, synthetic aperture radar (SAR) has been successfully used for mapping wetland flooding dynamics due to their all-weather acquisition capability and the ability to identify below canopy inundation processes. The increasing availability from current and planned SAR missions poses an opportunity to advance space-based wetland monitoring, but their operational implementation requires the development of flexible and scalable frameworks.This study aims to develop WetSAT-ML, a satellite-based machine learning (ML) framework for mapping wetland flooding dynamics and trends using Sentinel-1 SAR data. The approach combines radar features with supervised and unsupervised ML classification algorithms, to distinguish different inundation categories, including open water, vegetated water, and land. Random Forest and K-means models were trained using two training and validation areas in the South Florida Everglades (USA) and the lower Atrato River Basin (Colombia). These sites were selected considering their wetland variability and the availability of reference data. The first version of the models was trained using all available Sentinel-1 observations from 2024 over the selected regions, capturing the full hydrological seasonality. Reference training and validation datasets included gauge measurements and manually annotated data for both regions. The trained WetSAT-ML models are being evaluated through a proof of concept across five wetland systems in South Asia and South America, including the Meghna River wetlands in Bangladesh; the Atrato River, Ayapel, and Barbacoa wetlands in Colombia; and the Pantanal wetlands spanning the Brazil–Bolivia border. The test sites represent a wide range of hydroclimatic conditions, geomorphological settings, and vegetation cover.Preliminary results indicate that WetSAT-ML consistently captures spatial patterns and intra-annual inundation dynamics that are coherent with the known hydrological regimes of the test regions. Cross-site comparisons reveal clear differences in key hydroperiod parameters related to inundation persistence, seasonal amplitude, and ecosystem connectivity. Overall, the results provide a foundation for operational wetland monitoring applications. WetSAT-ML is open access, and the first public version is available in a GitHub repository: https://github.com/sei-latam/WETSAT_v2. The next steps of the research will focus on cross-validation using independent datasets and expanding the training database across additional wetland areas.

This brief outlines key concerns, policy gaps and recommendations on how Myanmar's wetland conservation efforts can benefit from inclusion of community-led approaches, which have been proven to achieve more sustainable livelihood outcomes, based on research in the Upper Chindwin River basin. Local communities in the Upper Chindwin basin in Myanmar are highly dependent on wetland ecosystems and their resources for sustenance and livelihoods. The research reported here is focused on wetland conservation in the Hkamti region of the Upper Chindwin basin. Promoting locally inclusive wetland conservation and management can lead to triple benefits: ensuring wetlands' wise use, achievement of biodiversity and climate targets, and livelihood security for dependent communities. It is therefore essential to integrate and enhance avenues for community stewardship in wetland conservation and management. Key messages: In the Upper Chindwin River basin, the authors observed a 25% loss in wetland area between 2000 and 2020, with implications for this key biodiversity area. Based on insights from SEI's initiative in the Upper Chindwin basin, community inclusion can strengthen wetland conservation efforts while ensuring livelihood security for local communities. The authors suggest a national framework for wetlands conservation or management that ensures local communities are integrated into such efforts. Inclusion of locally led wetland management within Myanmar's national directives, such as the National Biodiversity Strategies and Action Plan (NBSAP) and National Adaptation Plan (NAP), is key to safeguarding these critical ecosystems, supporting local livelihoods, and achieving climate and biodiversity targets.

The Mekong Basin faces significant challenges in integrated management of the water-energy nexus amidst the growing pressures of climate change. Rapid urbanization and industrial growth are escalating the demand for energy and water for both food production and industrial use. Hydropower development, a key energy source in the region, further complicates water flow and threatens ecosystem health. Climate change impacts, including erratic rainfall patterns, droughts, and flooding, exacerbate the stability of both water and energy systems. While research on the Water-Food-Energy (WFE) nexus has expanded in the past decade to better understand the interconnections across sectors and borders, the academic discourse surrounding the Water-Energy-Climate (WEC) nexus remains limited. This study assesses the knowledge gaps in implementing the WEC nexus in the Mekong Basin through a systematic review. A total of 3,399 manuscripts were identified from databases such as PubMed, IEEE, IWA Publishing, SpringerLink, ProQuest, and ScienceDirect, published between 2012 and 2024. Out of these, 60 manuscripts were included in the analysis, along with two relevant reports from the Mekong River Commission. The analysis reveals a steady increase in publications, with the highest number in 2021, indicating growing scholarly interest in the interlinkages between water, energy, and climate systems. The study identifies key knowledge gaps, including governance-related, technological and engineering challenges, ecosystem and nature-based solutions, and issues related to Gender Equality, Disability, and Social Inclusion (GEDSI). A major finding is the lack of coordinated and integrated planning across the Mekong countries, which hinders effective management of the WEC nexus. Insufficient technologies to support fish migration and maintain environmental flows threaten downstream ecosystems vital to local communities. Moreover, addressing climate change while optimizing water and energy use requires deeper exploration of comprehensive solutions. Socio-cultural norms also limit women’s participation in leadership and technical roles within water, energy, and climate management. Without applying a gender-inclusive approach, nexus governance risks deepening existing inequalities. The study concludes that addressing these gaps necessitates enhanced regional collaboration, improved governance and integrated policy frameworks, strengthened capacity-building efforts, better data-sharing mechanisms, advanced WEC modeling capabilities, and a more holistic approach to policy-making aligned with the sustainable development goals of the Mekong countries.

Riverbank erosion is one of the world’s major hazards in delta areas. The Mekong Delta of Asia is one among them which is facing many sediment-related issues, particularly riverbank erosion. Extreme flood events and sea level rise due to climate change increase the risk of riverbank and coastal erosion in the Mekong Delta. This study aims to assess riverbank stability using the BSTEM model, investigate the level of vulnerability of local communities to riverbank erosion and understand their adaptive strategies to cope with riverbank erosion problems. The study focuses on Kaoh Soutin (KS) and Ruessei Srok (RS) communes which are next to the Mekong River in the delta area of Cambodia. Linking with flow velocity and water level from the HEC-RAS 2D model, the BSTEM model was set up to examine riverbank stability at two locations in KS and two locations in RS. The study used soil samplings and the laboratory test to investigate critical shear stress and erodibility coefficient for the BSTEM model. The results indicate that river water level and groundwater level are crucial factors influencing the overall stability of the riverbank. Higher water levels result in increased confining pressure on the riverbank, leading to a higher factor of safety. Soil erosion also has significantly impacted the riverbank at the study location. The level of vulnerability in two communities was determined based on IPCC’s livelihood vulnerability index (LVI) and coping strategies were determined based on field survey questionnaires and focus groups interviewed. It is found that KS is slightly more vulnerable to riverbank erosion than RS, as indicated by LVI values of 0.49 and 0.46 for KS and RS, respectively. The Chi-square test was carried out to identify vulnerability indicators that are statistically different between KS and RS. The current adaptive strategies based on interviews include reducing expenses, resettlement, diversifying income sources, and seeking support from various entities, including local authorities, NGOs, and government interventions during riverbank erosion. Large-scale monitoring and modeling systems are necessary for developing early warning systems and identifying hotspots. Riverbank protections both infrastructure-and nature-based solutions and migration plans are required to support livelihood adaptation.

Understanding the relationship between communities and wetland ecosystems is imperative to facilitate the development of wetland management and conservation strategies that can effectively safeguard wetland health and sustain the flow of ecosystem services. To understand the knowledge, attitude, and perception of communities on wetland ecosystem services, a survey was conducted involving 133 households from 4 villages dependent on 5 wetlands within the Upper Chindwin Basin (UCB), northwestern Myanmar. Most of the respondents extracted wetland resources for subsistence and income. A total of 84% of the households depended on wetland fish for their primary protein consumption, while 70% (n = 94) collected fuelwood from wetlands for subsistence. The survey participants unanimously recognized the benefits of wetland ecosystem services (i.e., provisioning benefits), particularly for fish, food, fiber, fuel, natural medicines, ornamental resources, and minerals. A total of 97% of the participants lacked knowledge of any existing law or regulation that ensures wetland protection in Myanmar. Furthermore, 87% of the respondents concurred that the government has not adequately endeavored to promote awareness of wetland conservation in this remote area due to lack of capacity and resources. This study establishes a baseline for the region and recommends designing and implementing a community-centric wetland action plan. This action plan provides a self-sustaining and cost-effective approach to conserve wetlands and is crucial in enhancing the capacity of dependent communities to participate and eventually lead wetland management of UCB.

The Sittaung river basin (SRB) remains one of the least studied basins of Myanmar in terms of the assessment of the impact of climate change. As several reservoirs already exist in the basin, much research is needed to understand how projected climate change impacts rainfall, temperature, flows, domestic and agricultural demands, and hydropower generation. Given the limitation in observed data on the ground, a combination of satellite-derived meteorological data and digital elevation data is used to generate inputs to a Water Evaluation and Planning (WEAP) model. Five CMIP5 GCMs are used in the WEAP to assess the impact of climate change on the water, food, and energy production of the SRB for the baseline (BL: 1985–2014), near future (NF: 2021–2050), and far future (FF: 2051–2080) periods. The results indicate that the average temperature and rainfall are likely to increase in the future for the SRB. December and January are expected to be drier and warmer, whereas rainy months are expected to be wetter and warmer in the future. The BL flows (1091 m³/s) are expected to increase by 7–10% during NF and by 16–19% during FF at the basin outlet. Meanwhile, the unmet domestic demand during BL (1.3 MCM) is expected to decrease further by approximately 50% in the future. However, the unmet agricultural demand (667 MCM) for food production is estimated to increase from the BL by 11–15% during NF and by 14–19% during FF. Similarly, the total energy generation of nine hydropower projects (4.12 million MWh) is expected to increase by 9–11% during NF and by 16–17% during FF. Thus, the riverine flows are expected to increase in the future, thus positively impacting the domestic and hydropower sectors, whereas the unmet demands in the agricultural sector likely remain unsatisfied. These results will help the water, agriculture, and energy sectors to develop strategies to maximize benefits and cope with the impacts of climate change in the near and long-term future.

The wetlands of the lower Songkhram River basin are protected as a Ramsar site and a regional environmental asset, but they are under threat from numerous factors related to developmental activities. The lower Songkhram River is the last free-flowing tributary of the Mekong River and is a lifeline for over 14 000 households and a haven to many endemic biodiversity species. Local communities in the region have unique associations with the natural resources in the river basin, including the wetlands, and they perceive immense value in conserving these ecosystems. Natural resource policies lack specific guidelines and collaborative approaches for the protection, conservation and management of the wetland resources of the lower Songkhram River. The way forward should be led by a synchronized planning process, collaborative governance of the wetland resources, and empowerment of already existing local community groups.

Monitoring morphologically dynamic rivers over large spatial domains at an adequate frequency is essential for informed river management to protect human life, ecosystems, livelihoods, and critical infrastructures. Leveraging the advancements in cloud-based remote sensing data processing through Google Earth Engine (GEE), a web-based, freely accessible seasonal river morphological monitoring system for Ayeyarwady River, Myanmar was developed through a collaborative process to assess changes in river morphology over time and space. The monitoring system uses Landsat satellite data spanning a 31-year long period (1988–2019) to map river planform changes along 3881.4 km of river length including Upper Ayeyarwady, Lower Ayeyarwady, and Chindwin. It is designed to operate on a seasonal timescale by comparing pre-monsoon and post-monsoon channel conditions to provide timely information on erosion and accretion areas for the stakeholders to support planning and management. The morphological monitoring system was validated with 85 reference points capturing the field conditions in 2019 and was found to be reliable for operational use with an overall accuracy of 89%. The average eroded riverbank area was calculated at around 45, 101, and 134 km² for Chindwin, Upper Ayeyarwady, and Lower Ayeyarwady, respectively. The historical channel change assessment aided us to identify and categorize river reaches according to the frequency of changes. Six hotspots of riverbank erosion were identified including near Mandalay city, the confluence of Upper Ayeyarwady and Chindwin, near upstream of Magway city, downstream of Magway city, near Pyay city, and upstream of the Ayeyarwady delta. The web-based monitoring system simplifies the application of freely available remote sensing data over the large spatial domain to assess river planform changes to support stakeholders’ operational planning and prioritizing investments for sustainable Ayeyarwady River management.

The large-scale monitoring of riverbank erosion is challenging because of human, equipment, and financial limitations, particularly in developing countries. This study aims to detect riverbank erosion and identify riverbank erosion hotspots along the Mekong River in Cambodia. A riverbank erosion rate map was developed using satellite images from Landsat 5, 7, and 8 (1990–2020) using the modified normalized difference water index (MNDWI) at a resolution of 30 m and Sentinel-2 (2016–2021) using the normalized difference water index (NDWI) at a resolution of 10 m. Detecting riverbanks in satellite images using a water index depends greatly on image resolution and water threshold. The riverbank lines were validated using Google Earth images. In the data used in December 2017, the root mean square error (RMSE) of Sentinel-2 was 6.00 m, while the RMSE of Landsat was 6.04 m. In the data used in January 2019, the RMSE of Sentinel-2 was 4.12 m, while the RMSE of Landsat was 5.90 m. The hotspots were identified by overlaying the riverbank erosion rate map and the exposure map of population density and land cover. Field surveys and interviews were conducted to verify riverbank erosion hotspots in the Ruessei Srok and Kaoh Soutin communes. The results showed that within the last 30 years (1990–2020), the riverbank eroded more than 1 km in a direction perpendicular to the river in some segments of the Mekong River in Cambodia. The highest average annual erosion rate was in the Ruessei Srok Commune in Kampong Cham Province, at approximately 43 m/yr. Most eroded areas were farmland and rural residential areas. The riverbank hotspots are situated mainly in the lower part of the Mekong River, where the population is dense, and the erosion rate is high. Riverbank erosion hotspots with a very high impact level and ongoing active erosion include the Peam Kaoh Sna, Kampong Reab, Kaoh Soutin, and Ruessei Srok communes in Kampong Cham Province. This study provides an efficient tool for using satellite images to identify riverbank erosion hotpots in a large river basin. The riverbank erosion hotspot map is essential for decision-makers to prioritize interventions to reduce the risk of riverbank erosion and to improve the livelihood of the people residing along the Mekong River.