The Lower Mekong Basin (LMB) is biologically diverse, economically important, and home to about 65 million people. The region has undergone extensive environmental changes since the 1990s due to such factors as agricultural expansion and intensification, deforestation, more river damming, increased urbanization, growing human populations, expansion of industrial forest plantations, plus frequent natural disasters from flooding and drought. The Mekong river is also heavily used for human transportation, fishing, drinking water, and irrigation. This paper discusses use of pre-existing LULC maps from 1997 and 2010 to derive a LMB regional LULC change map for 9 classes per date using GIS overlay techniques. The change map was derived to aid SWAT hydrologic modeling applications in the LMB, given the 2010 map is currently used in multiple LMB SWAT models, whereas the 1997 map was previously used. The 2010 LULC map was constructed from Landsat and MODIS satellite data, while the 1997 map was from before the MODIS era and therefore based on available Landsat data. The 1997–2010 LULC change map showed multiple trends. Permanent agriculture had expanded in certain sub-basins into previously forested areas. Some agricultural areas were converted to industrial forest plantations. Extensive forest changes also occurred in some locations, such as areas changed to shifting cultivation or permanent crops. Also, the 1997 map under classified some urban areas, whereas the 2010 LULC map showed improved identification of such areas. LULC map accuracy were assessed for 213 randomly sampled locations. The 1997 and 2010 LULC maps showed high overall agreements with reference data exceeding 87%. The LULC change map yielded a moderately high level of overall agreement (78%) that improved to ∼83% once LULC classification scheme specificity was reduced (forests and agriculture were each mapped as singular classes). The change map regionally showed a 4% decrease in agriculture and a 4% increase in deciduous and evergreen forests combined, though deforestation hot spot areas also were evident. The project yielded LULC map data sets that are now available for aiding additional studies that assess LMB LULC change and the impacts such change may pose to water, agriculture, forestry, and disaster management efforts. More work is needed to map, quantify and assess LULC change since 2010 and to further update the 2010 LULC map currently used in the LMB SWAT models.

Savanna woody plants can store significant amounts of carbon while also providing numerous other ecological and socio-economic benefits. However, they are significantly under-represented in widely used tree cover datasets, due to mapping challenges presented by their complex landscapes, and the underestimation of woody plants by methods that exclude short stature trees and shrubs. In this study, we describe a Google Earth Engine (GEE) application and present test case results for mapping percent woody canopy cover (%WCC) over a large savanna area. Relevant predictors of %WCC include information derived from radar backscatter (Sentinel-1) and optical reflectance (Sentinel-2), which are used in conjunction with plot level %WCC measurements to train and evaluate random forest models. We can predict %WCC at 40 m pixel resolution for the full extent of Senegal with a root mean square error of ∼8% (based on independent sample evaluation). Further examination of model results provides insights into method stability and potential generalizability. Annual median radar backscatter intensity is determined to be the most important satellite-based predictor of %WCC in savannas, likely due to its relatively strong response to non-leaf structural components of small woody plants which remain mostly constant across the wet and dry season. However, the best performing model combines radar backscatter metrics with optical reflectance indices that serve as proxies for greenness, dry biomass, burn incidence, plant water content, chlorophyll content, and seasonality. The primary use of GEE in the methodology makes it scalable and replicable by end-users with limited infrastructure for processing large remote sensing data.

People, livelihoods, and infrastructure in Myanmar suffer from devastating monsoonal flooding on a frequent basis. Quick and effective management of flood risk relies on planning and preparedness to ensure the availability of supplies, shelters and emergency response personnel. The mandated government agency Department of Disaster Management (DDM) as well as local and international organizations play roles in producing, disseminating, and using accurate and timely information on flood risk. Currently, systematic flood risk maps are lacking, which leaves DDM to rely on inconsistent historic reports and local knowledge to inform their emergency planning. Although these types of knowledge are critical, they can be complemented to reduce bias and human error to planning processes and decisions. As such, the present situation has led to ineffective distribution of emergency response resources prior to flooding, leaving vulnerable populations less-than-prepared for inevitable flood events. Given these issues, we have developed a flood risk decision-support tool in collaboration with DDM. The tool uses surface water maps developed by the Joint Research Center (JRC), which were derived from more than 30 years of Landsat imagery. We have also incorporated population data, land cover data, and other information on flood exposure and vulnerability to create the first scalable and replicable Flood Risk Index (FRI) for flood risk reduction in Myanmar.

While the Earth observation (EO) data and geospatial information technology (GIT) are getting more open and accessible, lack of skilled human resources and institutional capacities are limiting effective applications in the Hindu Kush Himalayan (HKH) region. This paper aims to present the capacity building approach and applications designed to fill these gaps and empower decision makers and practitioners in using EO data and GIT through information education and training. The capacity building approach consists of four components: assessment, design, implementation, and monitoring (ADIM). The assessment component focuses on identifying the needs and priorities of capacity building for targeted groups or institutions. The design component develops training content in order to execute the plan in coordination with subject matter experts (SME). The implementation component executes the capacity building activity in any of these four formats—standard training, training of trainers, on-the-job training, and exposure learning. The monitoring component helps to identify the participants' expectations, learning achievements, and feedback so as to improve future capacity building events. In the application of ADIM, we conducted needs assessment in four countries, designed 26 types of capacity building contents and implemented 39 capacity building events. A range of thematic topics—from agriculture and food security, water resources and hydro-climatic disasters, land use, land cover and ecosystem, weather and climate services, to crosscutting issues—were covered in the events. Altogether, the activities reached out to over 1,000 individuals (35% of them women) from over 200 unique institutions in 30 countries. Institutional capacity was built for universities in Afghanistan and Bangladesh to design and deliver courses independently. The capacity of partner agencies were built to co-design and co-develop data and applications. The approach also experienced challenges in the nomination process and in identifying women participants due to the lack of women professionals in the field and in the respective agencies. The ADIM approach and its workflow focused on bridging the gap between the current trend and progression in EO and GIT fields and the existing state of capacity of the agencies involved in the decision-making process. It promoted gender equity, adopted frontier technologies, engaged SMEs and provided sustainable solutions, which are starting to bring success stories in the region.