Purpose
– While the top-down approach to design and implement post-disaster resettlement programmes are often influenced by spatial factors such as land availability and access to infrastructure facilities, failure to recognise socio-economic and cultural sensitivities of resettling communities have been noted as a common reason for unsuccessful resettlement programmes. Since these socio-economic and political issues are not mutually exclusive from spatial factors, the aim of this research is to develop a framework to assist the design and implementation of better post-disaster resettlement programmes through better coordination between spatial and socio-economic/cultural factors.
Design/methodology/approach
– An initial theoretical framework was developed through a comprehensive literature review followed by a validation through a case study approach.
Findings
– During the theoretical framework development, the differentiating priorities of policy maker's viewpoint and resettling community's viewpoints have been established as key theoretical constructs, within the emergency, transitional, and potential development phases of post-disaster resettlement programmes. Further, spatial analysis has been identified as an effective technique that can be used to investigate the interdependencies between the spatial, socio-economic and cultural factors within the post-disaster resettlement programmes. The case study findings confirmed that spatial analysis indeed can be used effectively to evaluate the above mentioned interdependencies within the context of post-debris flow event disaster resettlement programmes.
Originality/value
– It is expected that the developed framework can be used by authorities and policy makers who are designing and implementing resettlement programmes to evaluate how the spatial design of the programme can be used to minimise socio-economic and cultural issues of settling communities.
Technology has the potential to assist and directly address ecological topics. Our study compared efficiency and efficacy of foreground-detection computer vision and manual review methods for understanding behavior, natural history, and ecology of green pit vipers. Between 2015 and 2020 at the Sakaerat Biosphere Reserve in Thailand, 18,871 scans (pictures and timelapse recordings) and 1,507 minutes of continuous videos were recorded for 6 adult female, big-eyed pit vipers (Trimeresurus macrops). MotionMeerkat, an open source computer vision tool for finding ecological events in long video, appeared to be less efficient and overestimated presence of behaviors. Our study suggested that foreground-detection computer vision methods, exemplified by the program MotionMeerkat, can be free and open-source as well as easy to learn and use, but may not be as time efficient or fully address the complexity of ecological topics compared to review of images by human observers.
Land Surface Temperature (LST) estimation has been studied for several purposes, while the optimal method of estimating the LST has not been criticized yet. This research explores the optimum method in Land Surface Temperature (LST) estimation using LANDSAT-8 imagery data. Four different LST retrieval approaches, the Radiative Transfer Equation-based method (RTE), the Improved Mono-Window method (IMW), the Generalized Single-Channel method (GSC), and the Split-Window algorithm (SW), were calculated to present the LSTs over Buriram Town Municipality, Thailand. The calculated LSTs from these four methods were compared with the ground-based temperature data, taken on the same date and time of the employed LANDSAT-8 images. For this reason, the optimum method of the LST calculation was justified by considering the lowest normalized root means square error (NRMSE) values. As a result, the SW algorithm presents an optimum method in LST estimation. Regarding the SW, this algorithm requires not only the atmospheric profiles during satellite acquisition but also the retrieval of several coefficients. Besides, the LST retrieval method based on the SW algorithm is sensitive to water vapor content and coefficients. Although the SW algorithm is an optimum method explored in this study, it is emphasized that the adjustable values of coefficient response to the atmospheric state may be recommended. With these conditions, the SW algorithm can generate the land-surface temperature over the mixed land-use and land cover on the LANDSAT-8 images.
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