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How has education for refugees been shaped by broader dynamics of educational privatization? This paper argues that the invoking of the ‘refugee crisis’ narrative has been a crucial force in facilitating the privatization of this sector. The urgency of crisis helps to naturalize private actors’ participation in refugees’ education as equal partners to host governments, multilateral agencies, and civil society. Consistent with Stephen Ball’s (2012) distinction between privatization in and of education, the privatization of refugee education also advances through two dimensions: the creation of a new space – a new ‘market’ – for private actors, and the infusion of market and business principles such as ‘innovation’ into all aspects of education. The crisis narrative has created a new ‘horizon of taken-for-granted’ (Hall, 1993), where it is simply natural that private actors must participate in the assumption of the traditional responsibilities of the state in providing education for refugees.

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Broadleaf deciduous forests (BDFs) or dry dipterocarp forests play an important role in biodiversity conservation in tropical regions. Observations and classification of forest phenology provide valuable inputs for ecosystem models regarding its responses to climate change to assist forest management. Remotely sensed observations are often used to derive the parameters corresponding to seasonal vegetation dynamics. Data acquired from the Sentinel-1A satellite holds a great potential to improve forest type classification at a medium-large scale. This article presents an integrated object-based classification method by using Sentinel-1A and Landsat 8 OLI data acquired during different phenological periods (rainy and dry seasons). The deciduous forest and nondeciduous forest areas are classified by using NDVI (normalized difference vegetation index) from Landsat 8 cloud-free composite images taken during dry (from February to April) and rainy (from June to October) seasons. Shorea siamensis (S. siamensis), Shorea obtusa (S. obtusa), and Dipterocarpus tuberculatus (D. tuberculatus) in the deciduous forest area are classified based on the correlation between phenology of BDFs in Yok Don National Park and backscatter values of time-series Sentinel-1A imagery in deciduous forest areas. One hundred and five plots were selected during the field survey in the study area, consisting of dominant deciduous species, tree height, and canopy diameter. Thirty-nine plots were used for training to decide the broadleaf deciduous forest areas of the classified BDFs by the proposed method, and the other sixty-six plots were used for validation. Our proposed approach used the changes of backscatter in multitemporal SAR images to implement BDF classification mapping with acceptable accuracy. The overall accuracy of classification is about 79%, with a kappa coefficient of 0.7. Accurate classification and mapping of the BDFs using the proposed method can help authorities implement forest management in the future.