An ongoing humanitarian crisis exists in South Asia along the border of Myanmar and Bangladesh in Rakhine State, which is part of Myanmar. Long running and complex cultural, political, and religious drivers have resulted in hundreds of thousands of largely Muslim Rohingya leaving Rakhine. A large percentage of the exodus has ended up in Cox’s Bazar, Bangladesh and nearby refugee shelter camps while a small percentage have migrated to Indonesia, Thailand, Nepal, and Malaysia. Recent conflicts in October and November of 2016, followed by more conflicts during August to October 2017 and again throughout 2018, substantially increased departures with international organizations estimating more than 750,000 emigrations since these occurrences [1
]. Prior to these recent conflicts, approximately 1 million Rohingya resided in this region.
Part of this crisis is amplified by low food security with historical livelihoods tied to agriculture in this low-production, disaster-prone region [2
]. Little to no transparent or robust food security information is available for the region regarding crop production. The Food and Agriculture Organization (FAO) estimates 80% of the Rakhine population is under nourished or suffer malnutrition with three-quarters living below the poverty line [1
]. Many Rohingya—especially in the areas of Buthidaung, Maungdaw, Myinbya, Pauktauw, Sittwe, and Zawmadat—have abandoned their home and land including much of the male population, which tend to be the primary workers for crop production. About half the total population directly or indirectly participates in subsistence agriculture with most being paddy and some mixed rotation vegetables. FAO reports 90% suffer some form of hunger and this area remains one of the most food insecure locations in the world. Given the geopolitical circumstances, access to comprehensively assess food security has remained a challenge and mixed reports on field cultivation, ownership rights, and food production have created uncertainty and added to risks.
Mapping Food Security
Assessment of food security requires robust, timely, and transparent approaches and ability to respond to dynamic events. One approach to support assessment of food security is the use of satellite remote sensing. Many tools and approaches exist and often Earth Observation (EO) technology is used as part of a convergence of evidence framework that blends indices of landscape conditions with human and local knowledge. For example, the famine early warning systems network (FEWS NET) agroclimatological and satellite remote sensing approach typically relies on a convergence of evidence among three physically independent observational types, namely: landscape greenness (such as normalized difference vegetation index (NDVI)), evapotranspiration anomalies largely based on land surface temperature models, and ensemble gridded rainfall data, such as climate hazards infrared precipitation with stations [CHiRPS, 4]. In data-sparse regions like Myanmar, satellite remote sensing has been critical for supporting food security decision support tools [3
]. However, in this case, the main driver of food security is not agroclimatological forces; rather, conflict is the primary driver of risk. Therefore, being able to distinguish crop field activity and cultivation is the need and challenge.
In Myanmar, the vast majority (>75%) of rice is produced during the main wet season (May to October). A second rice crop, if amenable weather conditions, stable market prices, and labor exist, can potentially be sowed during November and December with harvest by May the following year. Maize, potatoes, beans, and grasses are the usual cash, food, and rotational crops that might be grown outside of the main rice season. Rice crops make up a major portion of diet calories and is a major driver of livelihoods. Rotations, fallow land use, and yield vary remarkably within season and season-to-season making operational assessment very challenging.
In the case of Rakhine, use of more-coarse (250 m–1 km) satellite sensors, such as MODIS, have limitations due to the patchy landscape given the small fields, subsistence practices, and spatially varying growth stages. For these reasons, moderate spatial resolution (<30 m) sensors, such as Landsat-8 and Sentinel-2, are required for more thorough assessment of crop conditions. Typically, here, the main rice crop is sowed between May to early June, ripens toward September, and is followed by harvest starting as early as October. As noted, this corresponds with the main monsoon rains which concordantly coincide with high cloud cover. Given the crop calendar, it is very difficult to acquire quality optical imagery in an optimal time range to discriminate crop conditions given the repeat frequency of Landsat (16-day window).
Synthetic aperture radar (SAR) instruments offer abilities to assess crops due to their all-weather capabilities and sensitivity to crop and field characteristics (i.e., dielectric constant, roughness, and orientation) different from those derived from optical instruments. Thanks to this, SAR imagery can be captured at the best suited dates to extract unique features. However, historically SAR data have been limited due to no consistent, large-area operational acquisition strategies at appropriate scales; poor-quality digital elevation models (DEMs) required for processing; complex data structures relative to optical data; and lack of standardized workflows. Even the more progressive data acquisition plans have not met the requirements for operational monitoring of crop landscapes. For example, the ALOS–1 L-band acquisition strategy acquired an image in ScanSAR mode once every 46 days for the same area. For crops in Rakhine (Figure 1
) this only provides a few images during the cropping season, making practical monitoring applications challenging.
More availability from the recent (2014 and 2016) launches of Sentinel-1A and -1B has created new opportunities for using moderate-resolution (<30 m) SAR for food security assessment. This is especially relevant in cloud-prone regions, as well as during critical growth stages when intermittent clouds limit optical coverage [3
]. However, an established approach of comparing current conditions to historical trends (e.g., 30-year climatology) or the past 5 years is not feasible given the relatively short archives. Further, the techniques for using automated SAR indices as indicators of food security are lacking compared to optical (i.e., NDVI anomalies).
Regardless of Landsat and Sentinel’s strength and limitations, these are the primary moderate-resolution, open access, and operational Earth Observation tools the remote sensing community has available for responding to “hot spot” food security crises. The overarching objective of this effort was to characterize recent production by categorizing fields into cultivated, fallow, or abandoned and illustrate how EO can be used to respond to food security events. Given security concerns, no robust quantitative ground truth data was available. Moderate-resolution Landsat-8 and Sentinel-2 optical imagery and Sentinel-1 radar imagery over the past three years (2016–2018) were used to classify cropland patterns. Exploratory analyses, data mining, time series analysis, thresholding, and qualitative assessments were all leveraged and applied to support a better understanding of food security in the region.