Coastal communities in Demak, Indonesia, face increasing socio-environmental challenges due to land subsidence, sea level rise, and economic vulnerability. This study assesses the socio-economic feasibility of green mussel (Perna viridis) cultivation on bamboo structures as a Nature-based Solution (NbS) for coastal protection and livelihood improvement. A structured survey was conducted among 337 respondents across 13 villages, focusing on demographic distribution, financial and market feasibility and key barriers. Using Latent Class Cluster Analysis, three distinct clusters were identified. Cluster 1 had balanced demographics and moderate interest in green mussel farming, with concerns over capital and poaching. Cluster 2 showed strong investment interest, easy market access, and highlighted poaching and licensing as key issues. Cluster 3 had the oldest, least educated group, low investment interest, and mentioned lack of capital and skills as key barriers. The study found that proximity to infrastructure and aquaculture exposure influence investment interest in green mussel farming. Younger, educated individuals and aquaculture workers showed more willingness to invest. These insights highlight the importance of tailoring NbS interventions to local socio-economic and geographic contexts. Targeted microcredit schemes, capacity building, community-based monitoring and participatory design processes are necessary to enhance the socio-economic feasibility of green mussel farming on bamboo structures.

Coastal lowland areas support much of the world population on only a small part of its terrestrial surface. Yet these areas face rapidly increasing land surface subsidence and flooding, and are most vulnerable to future sea level rise. The accurate and up to date digital terrain models (DTMs) that are required to predict and manage such risks are absent in many of the areas affected, especially in regions where populations are least developed economically and may be least resilient to such changes. Airborne LiDAR is widely seen as the most accurate data type for elevation mapping but can be prohibitively expensive, as are detailed field surveys across a broad geographic scale. We present an economical method that utilizes airborne LiDAR data along parallel flight lines ('strips') covering between 10% and 35% of the land depending on terrain characteristics, and manual interpolation. We present results for lowland areas in Central Kalimantan and East Sumatra (Indonesia), for which no accurate DTM currently exists. The study areas are covered with forest, plantations and agricultural land, on mineral soils and peatlands. The method is shown to yield DTM differences within 0.5 m, relative to full coverage LiDAR data, for 87.7-96.4% of the land surface in a range of conditions in 15 validation areas, and within 1.0 m for 99.3% of the area overall. After testing, the method was then applied to the entire eastern coastal zone of Sumatra, yielding a DTM at 100 m spatial resolution covering 7.1 Mha of lowland area from 1.45 Mha of effective LiDAR coverage. The DTM shows that 36.3%, or 2.6 Mha, of this area is below 2 m +MSL and, therefore, at risk of flooding in the near future as sea level rise continues. This DTM product is available for use in flood risk mapping, peatland mapping and other applications.