Poster - Mapping Opium Poppy Cultivation in Afghanistan Using Satellite Imagery

Abstract

Afghanistan is the world’s largest supplier of illicit opium, accounting for an estimated 70-80% of supply. In 2019, this generated an estimated income of $1.2-$2.1 billion domestically, or around 10% of Afghanistan’s gross domestic product. The illicit drug economy has provided livelihoods to millions of Afghans, but has also had numerous negative effects, including funding insurgent groups, exacerbating corruption and insecurity, and contributing to high domestic levels of drug addiction. From 2002 to 2017, the U.S. government spent over $8 billion on counter-narcotics efforts in Afghanistan, achieving little long-term success. The lack of reliable data has contributed to this failure; the robustness and interpretation of top-line estimates of area under cultivation have been questioned and criticized. Counter-narcotics efforts have focused on reducing total cultivation area, rather than trying to understand local socioeconomic or political conditions. The lack of granularity in official cultivation statistics has also impeded efforts by aid agencies to evaluate the impact of various interventions aimed at transitioning farmers away from poppy. Currently, official statistics on poppy cultivation are released annually by the United Nations Office on Drugs and Crime (UNODC) at a district level.1 These are produced using commercial high-resolution (0.5m × 0.5m) imagery, manually annotated by analysts and verified with ground imagery. In districts with substantial cultivation, a limited number of sites are sampled for labeling, while in other districts, all known cultivation areas are annotated. Only aggregate district-level cultivation figures are published and no detailed maps are available. The UNODC also conducts in-person surveys to characterize socioeconomic conditions. These methods, while undoubtedly valuable, are costly and difficult to undertake under poor security conditions. Furthermore, reports are released after long delays, with the government being suspected of blocking publication in some years. This paper investigates the possibility of using publicly available satellite imagery to generate poppy cultivation maps at high resolution. Some advantages of this source of data include its timeliness and cost-effectiveness, easy availability of data, and high level of granularity. These maps can then be combined with other data sources, such as grid-level data on climate, population, and healthcare, to further our quantitative understanding of the socioeconomic circumstances associated with poppy cultivation, a complex and persistent development challenge. This work complements official estimates, as well as related work that relies on commercial high-resolution satellite imagery, manual labelers, expert knowledge or qualitative methods. In developing these methods, we build on work using automated methods and spectral imagery to classify opium poppy, wheat, as well as other agricultural crops. In initial work, we limit our analysis to Helmand, a province accounting for more than half of all cultivation, where crop cycles are well-known and there are few major alternative crops. We carefully choose image acquisition dates based on crop cycles, and measure levels of vegetation growth in the pre- and post-harvest stages. We then apply a rule-based classification approach to infer areas under poppy cultivation, finding that our aggregate area estimates track official statistics closely at a district level (Pearson’s correlation ρ ≥ .8). Future work will involve refining the methodology and extending it to the rest of Afghanistan over multiple years. Early analysis suggests that this approach could generalize to other provinces in Southern and Western Afghanistan, but we expect to face more difficulty especially in the Northern parts of Afghanistan, due to smaller plot sizes, mixed cultivation patterns, complex terrain and the close proximity of agriculture to natural vegetation. Some potential solutions include automated strategies to infer best acquisition windows, and classifying agricultural land and opium poppy using more flexible approaches, such as unsupervised clustering methods. We hope that an extension of our current approach can provide additional quantitative insight to the local circumstances surrounding poppy cultivation, and ultimately contribute to the design of effective policies to protect the welfare of farmers while governments work towards their counter-narcotics goals.