Spatial-social evaluations of ecosystem services of adaptive aquaculture models using SAR and multivariate analyses: a case in the Vietnamese Mekong Delta.

The presented study is conducted to investigate the efficiency of two important aquaculture models of the Vietnamese Mekong Delta (VMD)'s Soc Trang province via quantifying and mapping the supporting ecosystem services (ES). The study targets the two most prevalent rearing practices, intensive and semi-intensive, covering four rural districts: My Xuyen, Tran De, Cu Lao Dung, and Vinh Chau. A mixed-method approach was applied, combining remote sensing, grass-root social survey, and multivariate statistical analyses. First, image analysis using Sentinel-1A time-series data was conducted to detect the aquaculture areas across the study area based on temporal changes of VV backscatter of different land use/land cover (LULC) types, in which aquaculture receives relatively low backscatter values compared to other LULC categories except river and deeper water surfaces. Our analysis yields an overall accuracy of 91% with a kappa coefficient of 0.82. Second, using semi-structured questionnaires, a total of 140 shrimp farming households across the four focused districts were interviewed for their rearing experience. Thereupon, the collected responses were analyzed using two multivariate analyses, including principal component analysis (PCA) and hierarchical cluster analysis (HCA). In general, the intensive model could have generated more economic values of ecosystem services than the artisanal/semi-intensive model. Our analyses also took note of the potential barriers hindering the semi-intensive farmers from achieving higher economic income. These include (i) geographical factors, i.e., locations; (ii) social factors, i.e., experience, farming calendar, education; (iii) financial factors, i.e., investments; and (iv) technical factors, i.e., farm areas, productivity, rearing concentration. Since semi-intensive food is more appropriate for small-scale farming households, it is recommendable that addressing these factors can enhance the efficiency of this model as a profitable livelihood option.

Factors affecting urban electricity consumption: a case study in the Bangkok Metropolitan Area using an integrated approach of earth observation data and data analysis.

Urbanization induces shifts in surface environmental factors, including impervious surface expansion, green space loss, and temperature increase in which the extreme temperature is supposed to significantly raise total electricity consumption (TEC) in urban areas. Applying remote sensing data and data analysis, this study aims to explore relationships between urbanization, surface environmental factors (SEF), and electricity consumption (EC). The relevance of surface temperature and total electricity consumption was also considered. The research found the disturbance of SEF through changes in vegetation index, urban index, and surface temperature. The vegetation was detected to be narrowed while the impervious surface and land surface temperature had the same trend of rising. These tendencies correspond to the urbanization process in the Bangkok Metropolitan Area (BMA). The urbanization process was also detected by extension of customers and electricity consumption, mainly in industrial sectors and household consumption. The number of users in industrial sectors well explained total consumption. Besides, the surface environmental factors jointly contributed to the consumption in the residential sector. Urban expansion assessed by urban index has more contribution to electricity utilization compared to surface temperature. These findings proved that the total consumption originated from the industrial sectors, especially the medium and large scales. These outcomes can serve the electrical business in order to provide adequate and improve service quality.