Coastal Erosion and Flood Coping Mechanisms in Southern Thailand: A Qualitative Study.

Communities in coastal regions are affected by the impacts of extreme climatic events causing flooding and erosion. Reducing the impacts of flood and erosion in these areas by adopting coping strategies that fortify the resilience of individuals and their localities is desirable. This study used summative content analysis to explore the coping mechanisms of coastal communities before, during, and after various dangers relating to flooding and erosion. The findings from the study show that effective surveillance systems, disaster preparedness, risk mapping, early warning systems, availability of databases and functional command systems, as well as reliable funding are essential to efficiently cope with hazards of coastal flooding and erosion. As flooding and erosion have been predicted to be more severe due to climate change in the coming years, the adoption of effective natural and artificial mechanisms with modern technologies could help coastal regions to be more resilient in coping with the dangers associated with flooding and erosion. Pragmatic policies and programs to this end by actors are critical to averting crises induced by flooding and erosion in coastal areas.

Coastal dynamism in Southern Thailand: An application of the CoastSat toolkit.

In Thailand, 17% of the population lives by the coast, approximately 11 million people. A combination of coastal erosion, sea level rise and coastal land subsidence are critical issues threatening the livelihoods of coastal communities. Thailand has invested a lot of money and installed conservation policies to restore and protect coastal mangroves and realign or replenish their beaches. This study assessed the use of the toolkit Coastsat to digitise a time series of shoreline positions from open access satellite images between 1990 and 2019 along 560 km of coastline in the provinces of Krabi and Nakhon Si Thammarat (NST). Based on these digitised shorelines and the use of the software Digital Shoreline Analysis System (DSAS), it was possible to identify shoreline change, which varied between -66 to +16.4 m/y in the mangroves of NST and -22.2 to +10.6 m/year on its sandy beaches. Shoreline change rates along the Krabi coast varied -34.5 to +21.7 m/year in the mangroves and -4.1 to +4 m/year on sandy beaches. Analysis of the spatial and temporal variations of the shoreline position during the survey period reveals a linkage between extreme weather conditions and coastal erosion along the NST coast while that linkage is less clear along the Krabi coast. CoastSat delivers crucial and accurate time series shoreline data over extensive areas that are vital to coastal managers and researchers in a completely remote manner, which is key with the presence of COVID-19 travel bans.

Socio-Economic Resilience to Floods in Coastal Areas of Thailand.

Krabi and Nakhon Si Thammarat are two coastal provinces in Thailand facing substantial threats from climate change induced hydrometeorological hazards, including enhanced coastal erosion and flooding. Human populations and livelihoods in these coastal provinces are at greater risk than those in inland provinces. However, little is known about the communities' resilience and coping capacities regarding hydrometeorological hazards of varying magnitudes. The study conducted a quantitative socio-economic assessment of how people in Krabi and Nakhon Si Thammarat provinces manage and respond to hydrometeorological hazards, examining their resilience and coping capacities. This was a cross-sectional study based on secondary data collection on the social and economic dimensions of resilience, and a review of literature on coping mechanisms to hydrometeorological hazards within the study area. Measuring and mapping socio-economic resilience was based on the available data gathered from the social and economic dimensions, with existing or standard indicators on exposure and vulnerability applied uniformly across subdistricts. A combination of social and economic dimensions produced novel socio-economic resilience index scores by subdistrict, which were mapped accordingly for the two coastal provinces. The study also derived a coping capacity index scores by combining availability of skills or soft capacity and availability of structural resources or hard coping capacity. Socio-economic resilience index scores varied greatly amongst subdistricts. Combining the soft and hard coping capacities, the average score across districts in both provinces was 3 out of a possible 4, meaning that most of the districts were largely resilient. However, variations also existed by subdistrict. Few subdistricts in both Krabi and Nakhon Si Thammarat provinces had low coping capacity index scores between 1 and 2 out of 4. District averages of socio-economic resilience scores mask the variations at subdistrict level. More studies with rigorous methodologies at village or neighborhood level is needed to obtain a nuanced understanding of community resilience to hydrometeorological hazards.

Testing the Contribution of Multi-Source Remote Sensing Features for Random Forest Classification of the Greater Amanzule Tropical Peatland.

Tropical peatlands such as Ghana's Greater Amanzule peatland are highly valuable ecosystems and under great pressure from anthropogenic land use activities. Accurate measurement of their occurrence and extent is required to facilitate sustainable management. A key challenge, however, is the high cloud cover in the tropics that limits optical remote sensing data acquisition. In this work we combine optical imagery with radar and elevation data to optimise land cover classification for the Greater Amanzule tropical peatland. Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission (SRTM) imagery were acquired and integrated to drive a machine learning land cover classification using a random forest classifier. Recursive feature elimination was used to optimize high-dimensional and correlated feature space and determine the optimal features for the classification. Six datasets were compared, comprising different combinations of optical, radar and elevation features. Results showed that the best overall accuracy (OA) was found for the integrated Sentinel-2, Sentinel-1 and SRTM dataset (S2+S1+DEM), significantly outperforming all the other classifications with an OA of 94%. Assessment of the sensitivity of land cover classes to image features indicated that elevation and the original Sentinel-1 bands contributed the most to separating tropical peatlands from other land cover types. The integration of more features and the removal of redundant features systematically increased classification accuracy. We estimate Ghana's Greater Amanzule peatland covers 60,187 ha. Our proposed methodological framework contributes a robust workflow for accurate and detailed landscape-scale monitoring of tropical peatlands, while our findings provide timely information critical for the sustainable management of the Greater Amanzule peatland.